TY - GEN
T1 - A phrase-based model to discover hidden factors and hidden topics in recommender systems
AU - Guan, Liping
AU - Alam, Md Hijbul
AU - Ryu, Woo Jong
AU - Lee, Sang-Geun
N1 - Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (2015R1A2A1A10052665).
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Recommender system
KW - Topic modeling
UR - http://www.scopus.com/inward/record.url?scp=84964644399&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964644399&partnerID=8YFLogxK
U2 - 10.1109/BIGCOMP.2016.7425942
DO - 10.1109/BIGCOMP.2016.7425942
M3 - Conference contribution
AN - SCOPUS:84964644399
T3 - 2016 International Conference on Big Data and Smart Computing, BigComp 2016
SP - 337
EP - 340
BT - 2016 International Conference on Big Data and Smart Computing, BigComp 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Big Data and Smart Computing, BigComp 2016
Y2 - 18 January 2016 through 20 January 2016
ER -