http://dx.doi.org/10.1007/978-3-642-38844-6_24">
 

Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

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

Language

English

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. © 2013 Springer-Verlag.

Keywords

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

Publication Date

2013

Source Publication Title

User Modeling, Adaptation, and Personalization: 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013 Proceedings

Start Page

281

End Page

288

Conference Location

Rome, Italy

Publisher

Springer

ISBN (print)

9783642388439

ISBN (electronic)

9783642388446

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