Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

7-2007

Abstract

Collaborative Filtering (CF) systems generate recommendations for a user by aggregating item ratings of other like-minded users. The memory-based approach is a common technique used in CF. This approach first uses statistical methods such as Pearson’s Correlation Coefficient to measure user similarities based on their previous ratings on different items. Users will then be grouped into different neighborhood depending on the calculated similarities. Finally, the system will generate predictions on how a user would rate a specific item by aggregating ratings on the item cast by the identified neighbors of his/her. However, current memory-based CF method only measures user similarities by simply looking at their rating trends while ignoring other aspects of overall rating patterns. To address this limitation, we propose a novel factor-based approach by incorporating user rating average, user rating variance, and number of overlapping ratings into the measurement of user similarity. The proposed method was empirically evaluated against the traditional memory-based CF method and other existing approaches including case amplification, significance weighting, and z-score using the MovieLens dataset. The results showed that the prediction accuracy of the proposed factor-based approach was significantly higher than existing approaches.

Discipline

Computer Sciences | Management Information Systems

Research Areas

Information Systems and Management

Publication

Intelligent Techniques for Web Personalization and Recommender Systems in E-Commerce: Papers from the 2007 AAAI Workshop, Vancouver, Canada

First Page

110

Last Page

117

ISBN

9781577353355

Publisher

AAAI Press

City or Country

Menlo Park, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://www.aaai.org/Papers/Workshops/2007/WS-07-08/WS07-08-013.pdf

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