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Abstract
A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To this end, we use so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. In addition, such viewpoints can be used as explanations. We apply a Gibbs EM sampler to infer posterior distributions of sCVR. Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations.
Original language | American English |
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Title of host publication | Social collaborative viewpoint regression with explainable recommendations |
Publisher | Association for Computing Machinery, Inc |
Pages | 485-494 |
Number of pages | 10 |
ISBN (Electronic) | 9781450346757 |
DOIs | |
State | Published - 2017 |
Event | 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom Duration: Feb 6 2017 → Feb 10 2017 |
Publication series
Name | WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining |
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Conference
Conference | 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 |
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Country/Territory | United Kingdom |
City | Cambridge |
Period | 02/6/17 → 02/10/17 |
Keywords
- Recommender systems
- Topic modeling
- Trusted social relations
- User comment analysis
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Construction of Method and Algorithm Knowledge Graphs at University College London (UCL) Big Data Institute
Lunagomez, S. (CoI), Collins, E. (CoI), Augenstein, I. (CoI), Riedel, S. (CoI), Maynard, D. (CoI), Montcheva, K. (CoI), Ling, E. (CoI) & Hobby, M. (CoI)
10/1/15 → 09/30/17
Project: Research