A Novel Recommendation Algorithm Incorporating Temporal Dynamics, Reviews and Item Correlation

Ting WU  Yong FENG  JiaXing SANG  BaoHua QIANG  YaNan WANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.8   pp.2027-2034
Publication Date: 2018/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7387
Type of Manuscript: PAPER
Category: Data Engineering, Web Information Systems
Keyword: 
matrix factorization,  time-aware recommender systems,  topic model,  item correlation,  

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Summary: 
Recommender systems (RS) exploit user ratings on items and side information to make personalized recommendations. In order to recommend the right products to users, RS must accurately model the implicit preferences of each user and the properties of each product. In reality, both user preferences and item properties are changing dynamically over time, so treating the historical decisions of a user or the received comments of an item as static is inappropriate. Besides, the review text accompanied with a rating score can help us to understand why a user likes or dislikes an item, so temporal dynamics and text information in reviews are important side information for recommender systems. Moreover, compared with the large number of available items, the number of items a user can buy is very limited, which is called the sparsity problem. In order to solve this problem, utilizing item correlation provides a promising solution. Although famous methods like TimeSVD++, TopicMF and CoFactor partially take temporal dynamics, reviews and correlation into consideration, none of them combine these information together for accurate recommendation. Therefore, in this paper we propose a novel combined model called TmRevCo which is based on matrix factorization. Our model combines the dynamic user factor of TimeSVD++ with the hidden topic of each review text mined by the topic model of TopicMF through a new transformation function. Meanwhile, to support our five-scoring datasets, we use a more appropriate item correlation measure in CoFactor and associate the item factors of CoFactor with that of matrix factorization. Our model comprehensively combines the temporal dynamics, review information and item correlation simultaneously. Experimental results on three real-world datasets show that our proposed model leads to significant improvement compared with the baseline methods.