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.
This work is supported by National Natural Science Foundation of China (No. 60573090, 60673139).
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Chen, D., Wang, D., Yu, G., Yu, F. (2007). A PLSA-Based Approach for Building User Profile and Implementing Personalized Recommendation. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_62
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DOI: https://doi.org/10.1007/978-3-540-72524-4_62
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