Recommendation for new users with partial preferences by integrating product reviews with static specifications

Citation data:

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 0302-9743, Vol: 7899 LNCS, Page: 281-288

Publication Year:
2013
Captures 11
Readers 11
Citations 6
Citation Indexes 6
Repository URL:
https://repository.hkbu.edu.hk/hkbu_staff_publication/48
DOI:
10.1007/978-3-642-38844-6_24
Author(s):
Wang, Feng; Pan, Weike; Chen, Li
Publisher(s):
Springer Nature; Springer
Tags:
Mathematics; Computer Science; aspect-level opinion mining; consumer reviews; New users; partial preferences; product recommendation; static specifications
conference paper description
Recommending products to new buyers is an important problem for online shopping services, since there are always new buyers joining a deployed system. In some recommender systems, a new buyer will be asked to indicate her/his preferences on some attributes of the product (like camera) in order to address the so called cold-start problem. Such collected preferences are usually not complete due to the user's cognitive limitation and/or unfamiliarity with the product domain, which are called partial preferences. The fundamental challenge of recommendation is thus that it may be difficult to accurately and reliably find some like-minded users via collaborative filtering techniques or match inherently preferred products with content-based methods. In this paper, we propose to leverage some auxiliary data of online reviewers' aspect-level opinions, so as to predict the buyer's missing preferences. The resulted user preferences are likely to be more accurate and complete. Experiment on a real user-study data and a crawled Amazon review data shows that our solution achieves better recommendation performance than several baseline methods. © 2013 Springer-Verlag.