A session-specific opportunity cost model for rank-oriented recommendation

Document Type

Article

Publication Date

10-1-2018

Abstract

Recommender systems are changing the way that people find information, products, and even other people. This paper studies the problem of leveraging the context of the items presented to the user in a user/system interaction session to improve the recommender system's ranking prediction. We propose a novel model that incorporates the opportunity cost of giving up the other items in the session and computes session-specific relevance values for items for context-aware recommendation. The model can work on a variety of different problems settings with emphasis on implicit user feedback as it supports varying levels of ordinal relevance. Experimental evaluation demonstrates the advantages of our new model with respect to the ranking quality.

Identifier

85052612874 (Scopus)

Publication Title

Journal of the Association for Information Science and Technology

External Full Text Location

https://doi.org/10.1002/asi.24044

e-ISSN

23301643

ISSN

23301635

First Page

1259

Last Page

1270

Issue

10

Volume

69

Grant

IIS-1322406

Fund Ref

National Science Foundation

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