An Efficient Solution to Factor Drifting Problem in the pLSA Model

Probabilistic latent semantic analysis (pLSA) is a powerful statistical technique to analyze relation between factors in dyadic data. Although various pLSA-based applications, ranging from information retrieval, information filtering, to text-mining and visualization, have been successfully conducte...

Full description

Saved in:
Bibliographic Details
Main Authors: Liang Zhang, Chaoran Li, Yanfei Xu, Baile Shi
Format: Conference Proceeding
Language:English
Subjects:
Chaos
Collaboration
Computational efficiency
Information analysis
Information filtering
Information filters
Information retrieval
Information technology
Maximum likelihood estimation
Pattern analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Probabilistic latent semantic analysis (pLSA) is a powerful statistical technique to analyze relation between factors in dyadic data. Although various pLSA-based applications, ranging from information retrieval, information filtering, to text-mining and visualization, have been successfully conducted, they can not afford dynamic revising of model when one of the factors changes constantly. In this paper, we take the advantage of decoupling ability of pLSA thoroughly, and propose a more elegant approach based on maximum likelihood estimation to gain an incremental learning with the drift of a factor. We demonstrate our method in the context of collaborative filtering where single user interests change fast, but the community interests remain almost constant. Experiments against the MovieLens and EachMovie data sets reveal that the proposed method improves the recommending accuracy 10% further beyond the original pLSA at a less computation cost
DOI:10.1109/CIT.2005.70