Maarten de Rijke

Identifying entity aspects in microblog posts

Publisher: ACM

Date: 12-08-2012

DOI: 10.1145/2348283.2348483

Block-Aware Item Similarity Models for Top- N Recommendation

Publisher: Association for Computing Machinery (ACM)

Date: 10-09-2020

DOI: 10.1145/3411754

Abstract: Top- N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of the item similarities in practice, we propose a block-diagonal regularization (BDR) over item similarities for ICF. The intuitions behind BDR are as follows: (1) with BDR, item clustering is embedded into the learning of ICF methods (2) BDR induces sparsity of item similarities, which guarantees recommendation efficiency and (3) BDR captures in-block transitivity to overcome rating sparsity. By regularizing the item similarity matrix of item similarity models with BDR, we obtain a block-aware item similarity model. Our experimental evaluations on a large number of datasets show that the block-diagonal structure is crucial to the performance of top- N recommendation.

Local Variational Feature-Based Similarity Models for Recommending Top- N New Items

Publisher: Association for Computing Machinery (ACM)

Date: 11-02-2020

DOI: 10.1145/3372154

Abstract: The top- N recommendation problem has been studied extensively. Item-based collaborative filtering recommendation algorithms show promising results for the problem. They predict a user’s preferences by estimating similarities between a target and user-rated items. Top- N recommendation remains a challenging task in scenarios where there is a lack of preference history for new items. Feature-based Similarity Models (FSMs) address this particular problem by extending item-based collaborative filtering by estimating similarity functions of item features. The quality of the estimated similarity function determines the accuracy of the recommendation. However, existing FSMs only estimate global similarity functions i.e., they estimate using preference information across all users. Moreover, the estimated similarity functions are linear hence, they may fail to capture the complex structure underlying item features. In this article, we propose to improve FSMs by estimating local similarity functions, where each function is estimated for a subset of like-minded users. To capture global preference patterns, we extend the global similarity function from linear to nonlinear, based on the effectiveness of variational autoencoders. We propose a Bayesian generative model, called the Local Variational Feature-based Similarity Model, to encapsulate local and global similarity functions. We present a variational Expectation Minimization algorithm for efficient approximate inference. Extensive experiments on a large number of real-world datasets demonstrate the effectiveness of our proposed model.

Overview of RepLab 2013: Evaluating Online Reputation Monitoring Systems

Publisher: Springer Berlin Heidelberg

Date: 2013

DOI: 10.1007/978-3-642-40802-1_31

Overview of RepLab 2014: Author Profiling and Reputation Dimensions for Online Reputation Management

Publisher: Springer International Publishing

Date: 2014

DOI: 10.1007/978-3-319-11382-1_24

Bayesian feature interaction selection for factorization machines

Publisher: Elsevier BV

Date: 2022

DOI: 10.1016/J.ARTINT.2021.103589

The Potential of Learned Index Structures for Index Compression

Publisher: ACM

Date: 11-12-2018

DOI: 10.1145/3291992.3291993

University Of Amsterdam

Location: Netherlands

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University Of Warwick

Location: United Kingdom of Great Britain and Northern Ireland

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Innovation Center For Artificial Intelligence

Location: Netherlands

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Universiteit Van Amsterdam

Location: Netherlands

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Ahold Delhaize

Location: Netherlands

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Centrum Wiskunde En Informatica

Location: Netherlands

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ARC Centres Of Excellence - Grant ID: CE200100005

Start Date: 08-2020

End Date: 08-2027

Amount: $31,783,576.00

Funder: Australian Research Council