Abstract
The majority of existing recommender systems focus on modeling the ratings; however, these systems ignore a large number of reviews. Existing rating based recommender systems are hard to discover the hidden dimensions in human feedback that can identify user preferences. In this study, we combine collaborative filtering with latent review topics to generate a new model called phraseHFT. We apply reviews to the phrase-level document, and use the phrase-based topic model to discover the review topics that are embedded in the review text. The interpretable topics which are learned and presented as phrases can help us understand characteristics of users or items. The conducted experiment shows that our approach outperforms the state-of-the-art techniques in perplexity and topic visualization due to the strong topic learning functionality.
Original language | English |
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Title of host publication | 2016 International Conference on Big Data and Smart Computing, BigComp 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 337-340 |
Number of pages | 4 |
ISBN (Electronic) | 9781467387965 |
DOIs | |
Publication status | Published - 2016 |
Event | International Conference on Big Data and Smart Computing, BigComp 2016 - Hong Kong, China Duration: 2016 Jan 18 → 2016 Jan 20 |
Publication series
Name | 2016 International Conference on Big Data and Smart Computing, BigComp 2016 |
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Other
Other | International Conference on Big Data and Smart Computing, BigComp 2016 |
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Country/Territory | China |
City | Hong Kong |
Period | 16/1/18 → 16/1/20 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Collaborative filtering
- Recommender system
- Topic modeling
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Information Systems and Management