ORCID Profile
0000-0001-6343-1455
Current Organisation
Hong Kong University of Science and Technology
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Publisher: IEEE
Date: 04-2020
Publisher: IEEE
Date: 2007
Publisher: Association for Computing Machinery (ACM)
Date: 09-03-2015
DOI: 10.1145/2699670
Abstract: Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests. To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top- k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 07-2003
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: IEEE Comput. Soc
Date: 1999
Publisher: Association for Computing Machinery (ACM)
Date: 10-10-2016
DOI: 10.1145/2873055
Abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, the Topic-Region Model (TRM) , to simultaneously discover the semantic, temporal, and spatial patterns of users’ check-in activities, and to model their joint effect on users’ decision making for selection of POIs to visit. To demonstrate the applicability and flexibility of TRM, we investigate how it supports two recommendation scenarios in a unified way, that is, hometown recommendation and out-of-town recommendation. TRM effectively overcomes data sparsity by the complementarity and mutual enhancement of the erse information associated with users’ check-in activities (e.g., check-in content, time, and location) in the processes of discovering heterogeneous patterns and producing recommendations. To support real-time POI recommendations, we further extend the TRM model to an online learning model, TRM-Online, to track changing user interests and speed up the model training. In addition, based on the learned model, we propose a clustering-based branch and bound algorithm (CBB) to prune the POI search space and facilitate fast retrieval of the top- k recommendations. We conduct extensive experiments to evaluate the performance of our proposals on two real-world datasets, including recommendation effectiveness, overcoming the cold-start problem, recommendation efficiency, and model-training efficiency. The experimental results demonstrate the superiority of our TRM models, especially TRM-Online, compared with state-of-the-art competitive methods, by making more effective and efficient mobile recommendations. In addition, we study the importance of each type of pattern in the two recommendation scenarios, respectively, and find that exploiting temporal patterns is most important for the hometown recommendation scenario, while the semantic patterns play a dominant role in improving the recommendation effectiveness for out-of-town users.
Publisher: Springer Science and Business Media LLC
Date: 04-09-2008
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11563952_9
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 24-04-2020
Publisher: Springer Science and Business Media LLC
Date: 05-10-2011
Publisher: ACM
Date: 30-04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 11-10-2016
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 04-02-2013
Publisher: Springer Science and Business Media LLC
Date: 03-2011
Publisher: IGI Global
Date: 04-2011
Abstract: The description of the origins of a piece of data and the transformations by which it arrived in a database is termed the data provenance. The importance of data provenance has already been widely recognized in database community. The two major approaches to representing provenance information use annotations and inversion. While annotation is metadata pre-computed to include the derivation history of a data product, the inversion method finds the source data based on the situation that some derivation process can be inverted. Annotations are flexible to represent erse provenance metadata but the complete provenance data may outsize data itself. Inversion method is concise by using a single inverse query or function but the provenance needs to be computed on-the-fly. This paper proposes a new provenance representation which is a hybrid of annotation and inversion methods in order to achieve combined advantage. This representation is adaptive to the storage constraint and the response time requirement of provenance inversion on-the-fly.
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 03-2009
DOI: 10.1109/ICDE.2009.17
Publisher: IEEE
Date: 04-2019
Publisher: IEEE
Date: 02-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: Springer International Publishing
Date: 2016
Publisher: Association for Computing Machinery (ACM)
Date: 22-02-2018
Abstract: We outline a call to action for promoting empiricism in data quality research. The action points result from an analysis of the landscape of data quality research. The landscape exhibits two dimensions of empiricism in data quality research relating to type of metrics and scope of method. Our study indicates the presence of a data continuum ranging from real to synthetic data, which has implications for how data quality methods are evaluated. The dimensions of empiricism and their inter-relationships provide a means of positioning data quality research, and help expose limitations, gaps and opportunities.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 13-10-2017
Publisher: Springer Science and Business Media LLC
Date: 25-05-2018
Publisher: Springer Science and Business Media LLC
Date: 14-08-2019
Publisher: IEEE
Date: 04-2020
Publisher: IEEE
Date: 06-2017
DOI: 10.1109/ICWS.2017.69
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: ACM
Date: 17-10-2015
Publisher: ACM
Date: 24-10-2011
Publisher: ACM
Date: 21-10-2023
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2000
DOI: 10.1142/S0218843000000065
Abstract: Spatial data, ranging from various land information data to different types of environmental data, are typically collected and used by different custodians. The full benefits of using spatial data can be achieved by combining the data from different sources covering a common region. Due to organizational, political and technical reasons, it is unrealistic to physically integrate the vast amount of spatial data managed by different systems in different organizations. A practical approach is to provide interoperability to support multi-site data queries. In this paper, we study the performance aspect of complex spatial query processing. We propose a framework for processing queries with multiple spatial and aspatial predicates using data from multiple sites. Using a new concept called generalized filter, a query is processed in three steps. First, an aspatial filter that incorporates some conditions derived from spatial predicates is used to find a set of candidates, which is a superset of the final query results. Then, the candidates are manipulated and a refinement step is executed following an optimized candidate sequence. Finally, a post-processing step is used to handle spatial expressions in query results. The focus of this paper is to generate enhanced filters in order to minimize the need of transferring and processing complex spatial data.
Publisher: Springer Science and Business Media LLC
Date: 04-04-2014
Publisher: Springer Science and Business Media LLC
Date: 07-2018
Publisher: ACM
Date: 30-01-2019
Publisher: Association for Computing Machinery (ACM)
Date: 11-2018
DOI: 10.1145/3230706
Abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users’ check-in data in LBSNs has a low s ling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models (2) there are various temporal cyclic patterns (i.e., daily, weekly, and monthly) in LBSNs, but existing work is limited to one specific pattern and (3) there is no existing framework that unifies users’ personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner. In light of the above challenges, we propose a Temporal Personalized Model ( TPM ), which introduces a novel latent variable topic-region to model and fuse sequential influence, cyclic patterns with personal interests in the latent and exponential space. The advantages of modeling the temporal effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity, and a direct expression of the semantic meaning of users’ spatial activities. Moreover, we introduce two methods to model the effect of various cyclic patterns. The first method is a time indexing scheme that encodes the effect of various cyclic patterns into a binary code. However, the indexing scheme faces the data sparsity problem in each time slice. To deal with this data sparsity problem, the second method slices the time according to each cyclic pattern separately and explores these patterns in a joint additive model. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top- k recommendation process by extending the traditional LSH. We evaluate the performance of TPM on two real datasets and one large-scale synthetic dataset. The performance of TPM in recommending cold-start items is also evaluated. The results demonstrate a significant improvement in TPM’s ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: Springer Science and Business Media LLC
Date: 09-10-2019
Publisher: Springer Science and Business Media LLC
Date: 21-03-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 06-06-2010
Publisher: Springer Science and Business Media LLC
Date: 07-01-2019
Publisher: IEEE
Date: 04-2015
Publisher: China Science Publishing & Media Ltd.
Date: 28-10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: IEEE
Date: 1999
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 02-05-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: ACM
Date: 12-06-2011
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE Comput. Soc
Date: 2004
Publisher: Association for Computing Machinery (ACM)
Date: 17-10-2013
Publisher: IEEE
Date: 04-2019
Publisher: Springer Science and Business Media LLC
Date: 23-04-2008
Publisher: PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO.
Date: 2005
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 04-2017
DOI: 10.1109/ICDE.2017.43
Publisher: ACM
Date: 29-10-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 15-10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 04-2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 27-01-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2010
DOI: 10.1109/TKDE.2009.68
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 22-12-2010
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 02-09-2020
Publisher: Springer Science and Business Media LLC
Date: 03-09-2016
Publisher: Performance Studies international
Date: 19-06-2023
Abstract: Where should we begin a dialogue about decolonizing and reimagining performance pedagogies in the Southeast Asian context? We first met when we were completing the Choreomundus International MA in Dance Knowledge, Practice, and Heritage in Europe and we are now pursuing our PhD degrees in Australia and Canada. Our dialogue opens by offering a contextualization of land grabbing and the exploitation of resources in the Philippines before delving into the relationship between land and body through careful reflection on our own bodily training. We are convinced that violence against land and body can be undone only through Indigenous sovereignty, by mobilizing the intergenerational knowledge that resides in Indigenous bodies. Decolonizing pedagogies enable an unlearning of the ways in which colonialism has been written on bodies. Integration as a decolonial method is not only about integrating performance elements, but is also about shifting towards a pedagogy of performance that empowers the Indigenous, minorities, and the marginalized, and integrates their clamor for land, social justice, and equity in the process of recreating cultural performances. By situating our personal journeys within the social and political contexts of our home countries and of the countries in which we have been educated, we hope that this dialogue offers an intimate consideration of what non-Indigenous and postcolonial scholars can contribute to the conversation around decolonizing performance pedagogies.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
Publisher: IEEE
Date: 05-2016
Publisher: IEEE
Date: 04-2018
Publisher: Springer Science and Business Media LLC
Date: 14-08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: ACM
Date: 27-05-2015
Publisher: IEEE
Date: 05-2016
Publisher: Springer Science and Business Media LLC
Date: 23-06-2015
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: Springer Science and Business Media LLC
Date: 24-10-2013
Publisher: Springer International Publishing
Date: 2015
Publisher: Association for Computing Machinery (ACM)
Date: 25-07-2016
DOI: 10.1145/2905373
Abstract: Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones. In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users’ check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users’ check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2003
Publisher: IEEE
Date: 04-2015
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer International Publishing
Date: 2018
Publisher: Association for Computing Machinery (ACM)
Date: 20-04-2017
DOI: 10.1145/3011019
Abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid . To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.
Publisher: IEEE
Date: 06-2019
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 29-09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Association for Computing Machinery (ACM)
Date: 05-2009
Abstract: Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video search engines to facilitate retrieving relevant clips. Different from traditional long videos, a video clip is a short video often expressing a moment of significance. Due to the high complexity of video data, efficient video clip search from large databases turns out to be very challenging. We propose a novel video clip representation model called the Bounded Coordinate System (BCS), which is the first single representative capturing the dominating content and content—changing trends of a video clip. It summarizes a video clip by a coordinate system, where each of its coordinate axes is identified by principal component analysis (PCA) and bounded by the range of data projections along the axis. The similarity measure of BCS considers the operations of translation, rotation, and scaling for coordinate system matching. Particularly, rotation and scaling reflect the difference of content tendencies. Compared with the quadratic time complexity of existing methods, the time complexity of measuring BCS similarity is linear. The compact video representation together with its linear similarity measure makes real-time search from video clip collections feasible. To further improve the retrieval efficiency for large video databases, a two-dimensional transformation method called Bidistance Transformation (BDT) is introduced to utilize a pair of optimal reference points with respect to bidirectional axes in BCS. Our extensive performance study on a large database of more than 30,000 video clips demonstrates that BCS achieves very high search accuracy according to human judgment. This indicates that content tendencies are important in determining the meanings of video clips and confirms that BCS can capture the inherent moment of video clip to some extent that better resembles human perception. In addition, BDT outperforms existing indexing methods greatly. Integration of the BCS model and BDT indexing can achieve real-time search from large video clip databases.
Publisher: Springer International Publishing
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: IEEE
Date: 2001
Publisher: ACM
Date: 23-07-2007
Publisher: Springer Science and Business Media LLC
Date: 04-09-2008
Publisher: IEEE
Date: 04-2020
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2010
Publisher: IEEE
Date: 05-2016
Publisher: IEEE
Date: 04-2008
DOI: 10.1109/INGS.2008.16
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2013
Publisher: ACM
Date: 03-11-2019
Publisher: Springer New York
Date: 2018
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user's check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.
Publisher: Association for Computing Machinery (ACM)
Date: 08-2017
Abstract: On time-dependent graphs, fastest path query is an important problem and has been well studied. It focuses on minimizing the total travel time (waiting time + on-road time) but does not allow waiting on any intermediate vertex if the FIFO property is applied. However, in practice, waiting on a vertex can reduce the time spent on the road (for ex le, resuming traveling after a traffic jam). In this paper, we study how to find a path with the minimal on-road time on time-dependent graphs by allowing waiting on some predefined parking vertices. The existing works are based on the following fact: the arrival time of a vertex v is determined by the arrival time of its in-neighbor u , which does not hold in our scenario since we also consider the waiting time on u if u allows waiting. Thus, determining the waiting time on each parking vertex to achieve the minimal on-road time becomes a big challenge, which further breaks FIFO property. To cope with this challenging problem, we propose two efficient algorithms using minimum on-road travel cost function to answer the query. The evaluations on multiple real-world time-dependent graphs show that the proposed algorithms are more accurate and efficient than the extensions of existing algorithms. In addition, the results further indicate, if the parking facilities are enabled in the route scheduling algorithms, the on-road time will reduce significantly compared to the fastest path algorithms.
Publisher: ACM
Date: 26-10-2010
Publisher: IEEE
Date: 04-2018
Publisher: Springer International Publishing
Date: 2017
Publisher: Association for Computing Machinery (ACM)
Date: 11-2010
Abstract: Recently, video search reranking has been an effective mechanism to improve the initial text-based ranking list by incorporating visual consistency among the result videos. While existing methods attempt to rerank all the in idual result videos, they suffer from several drawbacks. In this article, we propose a new video reranking paradigm called cluster-based video reranking (CVR). The idea is to first construct a video near-duplicate graph representing the visual similarity relationship among videos, followed by identifying the near-duplicate clusters from the video near-duplicate graph, then ranking the obtained near-duplicate clusters based on cluster properties and intercluster links, and finally for each ranked cluster, a representative video is selected and returned. Compared to existing methods, the new CVR ranks clusters and exhibits several advantages, including superior reranking by utilizing more reliable cluster properties, fast reranking on a small number of clusters, erse and representative results. Particularly, we formulate the near-duplicate cluster identification as a novel maximally cohesive subgraph mining problem. By leveraging the designed cluster scoring properties indicating the cluster's importance and quality, random walk is applied over the near-duplicate cluster graph to rank clusters. An extensive evaluation study proves the novelty and superiority of our proposals over existing methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-0100
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
DOI: 10.1109/TKDE.2013.65
Publisher: IEEE
Date: 04-2017
DOI: 10.1109/ICDE.2017.23
Publisher: ACM
Date: 11-2011
Publisher: Springer Berlin Heidelberg
Date: 2001
Publisher: Central Library of the Slovak Academy of Sciences
Date: 2017
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 17-05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-01-2021
DOI: 10.36227/TECHRXIV.13655597.V1
Abstract: Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an in idual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata. /
Publisher: Springer Science and Business Media LLC
Date: 22-10-2021
Publisher: ACM
Date: 13-10-2015
Publisher: Association for Computing Machinery (ACM)
Date: 05-06-2018
Abstract: Navigation has been an important tool for human civilization for thousands of years, and the latest technologies like online map services and GPS satellites have brought it up to a new level. Now people can easily identify where we are on earth, find any places they want to go, and retrieve best routes to get there. Although there are plenty of tools that are convenient and fast enough for basic uses, it is still far from optimal. For ex le, most systems only consider various type of distances as the optimization goals, while the traveling time, which needs to consider traffic conditions, is a more appropriate one. However, it is both hard to acquire the traffic condition information and to compute time-dependent fastest paths. Therefore, in this article, we present an introduction to the time-dependent route scheduling, from speed profile generation to route scheduling and query answering.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: ACM
Date: 17-10-2015
Publisher: Springer Science and Business Media LLC
Date: 07-04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 07-2014
DOI: 10.1109/MDM.2014.38
Publisher: ACM
Date: 26-10-2008
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Springer Science and Business Media LLC
Date: 27-10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 04-2010
Publisher: Springer Science and Business Media LLC
Date: 24-08-2019
Publisher: Elsevier BV
Date: 10-2016
Publisher: Springer Science and Business Media LLC
Date: 10-06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: IEEE
Date: 07-2014
DOI: 10.1109/MDM.2014.48
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 04-2015
Publisher: Springer Science and Business Media LLC
Date: 26-06-2007
Publisher: IEEE
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 07-2015
Publisher: ACM
Date: 29-09-2007
Publisher: ACM
Date: 06-11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: Springer Berlin Heidelberg
Date: 2005
Publisher: IEEE
Date: 2007
Publisher: Routledge
Date: 14-03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: IEEE
Date: 05-2016
Publisher: Springer Science and Business Media LLC
Date: 03-06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2012
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 04-2018
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-01-2021
DOI: 10.36227/TECHRXIV.13655597
Abstract: Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an in idual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata. /
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2017
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation. Specifically, FDSA first integrates various heterogeneous features of items into feature sequences with different weights through a vanilla mechanism. After that, FDSA applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Then, we integrate the outputs of these two blocks to a fully-connected layer for next item recommendation. Finally, comprehensive experimental results demonstrate that considering the transition relationships between features can significantly improve the performance of sequential recommendation.
Publisher: IEEE Comput. Soc
Date: 1999
Publisher: IEEE
Date: 04-2018
Publisher: ACM
Date: 19-10-2009
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Traffic speed prediction is known as an important but challenging problem. In this paper, we propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction than existing solutions. It takes advantage of both RNN and CNN models by a rational integration of them, so as to learn more meaningful time-series patterns that can adapt to the traffic dynamics of surrounding areas. Furthermore, since traffic evolution is restricted by the underlying road network, a network embedded convolution structure is proposed to capture topology aware features. The fusion with other information, including periodicity and context factors, is also considered to further improve accuracy. Extensive experiments on two real datasets demonstrate that our proposed LC-RNN outperforms six well-known existing methods.
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2014
Publisher: Association for Computing Machinery (ACM)
Date: 11-04-2023
DOI: 10.1145/3577928
Abstract: Sequential recommendation has been a widely popular topic of recommender systems. Existing works have contributed to enhancing the prediction ability of sequential recommendation systems based on various methods, such as recurrent networks and self-attention mechanisms. However, they fail to discover and distinguish various relationships between items, which could be underlying factors which motivate user behaviors. In this article, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning. At the global level, we build a global-link graph over all sequences to model item relationships. Then a channel-aware disentangled learning layer is designed to decompose edge information into different channels, which can be aggregated to represent the target item from its neighbors. At the local level, we apply a variational auto-encoder framework to learn user intention over the current sequence. We evaluate our proposed method on three real-world datasets. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines and is able to distinguish item features.
No related grants have been discovered for Xiaofang Zhou.