Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
An Efficient Solution to Factor Drifting Problem in the pLSA Model
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Using Incremental PLSI for Threshold-Resilient Online Event Analysis
IEEE Transactions on Knowledge and Data Engineering
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
Incremental collaborative filtering for highly-scalable recommendation algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Adaptive Bayesian Latent Semantic Analysis
IEEE Transactions on Audio, Speech, and Language Processing
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PLSA which was originally introduced in text analysis area, has been extended to predict user ratings in the collaborative filtering context, known as Triadic PLSA (TPLSA). It is a promising recommender technique but the computational cost is a bottleneck for huge data set. We design a incremental learning scheme for TPLSA for collaborative filtering task that could make forced prediction and free prediction as well. Our incremental implementation is the first of its kind in the probabilistic model based collaborative filtering area, to our best knowledge. Its effectiveness is validated by experiments designed for both rating-based and ranking-based collaborative filtering.