Folksonomy-based fuzzy user profiling for improved recommendations

Title

Folksonomy-based fuzzy user profiling for improved recommendations

Creator

Anand D.; Mampilli B.S.

Description

Genre is a major factor influencing user decisions to peruse an item in domains such as movies, books etc. Recommender systems, generally have, at their disposal, information regarding genres/categories that a movie/book belongs to. However, the degree of membership of the objects in these categories is typically unavailable. Such information, if available, would provide a better description of items and consequently lead to quality recommendations. In this paper, we propose an approach to infer the degree of genre presence in a movie by examining the various tags conferred on them by various users. Tags are user-defined metadata for items and embed abundant information about various facets of user likes, their opinion on the quality and the type of object tagged. Leveraging on tags to guide the genre degree determination exploits crowd sourcing to enrich item content description. Fuzzy logic naturally models human logic allowing for the nuanced representation of features of objects and thus is utilized to derive such gradual representation as well as for modeling user profiles. To the best of our knowledge ours is one of the first approaches to utilize such folksonomy information to infer genre degrees subsequently used for recommendations. The proposed method has the twin advantages of utilizing enriched content information for recommendation as well as squeezing the information from the user-item-tag and user-item ratings spaces and condensing them into fuzzy user profiles. The fuzzy user and object representations are leveraged both for the design of content-based as well as collaborative recommender systems. Experimental evaluations establish the effectiveness of the proposed approaches as compared to other baselines. 2013 Elsevier Ltd. All rights reserved.

Source

Expert Systems with Applications, Vol-41, No. 5, pp. 2424-2436.

Date

2014-01-01

Subject

Content based recommender systems; Folksonomy; Fuzzy sets; Reclusive methods; Recommender system; Tags

Coverage

Anand D., CMR Institute of Technology, AECS Layout, Bangalore, Karnataka 560037, India; Mampilli B.S., Christ University, Bangalore, Karnataka 560029, Hosur Road, India

Rights

Restricted Access

Relation

ISSN: 9574174; CODEN: ESAPE

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.eswa.2013.09.041

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