A PLSA-based approach for building user profile and implementing personalized recommendation

Authors:
Dongling Chen;Daling Wang;Ge Yu;Fang Yu
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, P.R. China and School of Information, Shenyang University, Shenyang, P.R. China;School of Information Science and Engineering, Northeastern University, Shenyang, P.R. China;School of Information Science and Engineering, Northeastern University, Shenyang, P.R. China;School of Information Science and Engineering, Northeastern University, Shenyang, P.R. China
Venue:
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Year:
2007

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Abstract

This paper proposes a method based on Probability Latent Semantic Analysis (PLSA) to analyze web pages that are of interest to the user and the user query co-occurrence relationship, and utilize the latent factors between the two co-occurrence data for building user profile. To make the weight of web pages that user isn't interested decay rapidly, a Fibonacci function is designed as the decay factor for representing the user's interests more exactly. The personalized recommendation is implemented according to the score of web pages. The experimental results showed that our approach was more effective than the other typical approaches to construct user profile.