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

Feng Wang, Weike Pan, Li CHEN

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation and Personalization - 21st International Conference, UMAP 2013, Proceedings
Pages281-288
Number of pages8
DOIs
Publication statusPublished - 2013
Event21st International Conference on User Modeling, Adaptation and Personalization, UMAP 2013 - Rome, Italy
Duration: 10 Jun 201314 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7899 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on User Modeling, Adaptation and Personalization, UMAP 2013
Country/TerritoryItaly
CityRome
Period10/06/1314/06/13

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • aspect-level opinion mining
  • consumer reviews
  • New users
  • partial preferences
  • product recommendation
  • static specifications

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