Explicit Factor Models for explainable recommendation based on phrase-level sentiment analysis

Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, Shaoping Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

344 Scopus citations

Abstract

Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations. In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sentiment analysis on user reviews, then generate both recommendations and disrecommendations according to the specific product features to the user's interests and the hidden features learned. Besides, intuitional feature-level explanations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more inuential on user's purchasing behavior

Original languageEnglish (US)
Title of host publicationSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages83-92
Number of pages10
ISBN (Print)9781450322591
DOIs
StatePublished - 2014
Externally publishedYes
Event37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia
Duration: Jul 6 2014Jul 11 2014

Publication series

NameSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
Country/TerritoryAustralia
CityGold Coast, QLD
Period7/6/147/11/14

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Information Systems

Keywords

  • Collaborative Filtering
  • Recommendation explanation
  • Recommender systems
  • Sentiment analysis

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