A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
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
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
A note on the utility of incremental learning
AI Communications
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
Recommending questions using the mdl-based tree cut model
Proceedings of the 17th international conference on World Wide Web
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Incremental aspect models for mining document streams
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Adaptive Bayesian Latent Semantic Analysis
IEEE Transactions on Audio, Speech, and Language Processing
Online New Event Detection Based on IPLSA
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Donation dashboard: a recommender system for donation portfolios
Proceedings of the third ACM conference on Recommender systems
Accelerating instant question search with database techniques
Proceedings of the 20th international conference companion on World wide web
RPLSA: A novel updating scheme for Probabilistic Latent Semantic Analysis
Computer Speech and Language
Improving question recommendation by exploiting information need
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Gaussian process for recommender systems
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Dual role model for question recommendation in community question answering
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Term Weighting Schemes for Emerging Event Detection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
CQArank: jointly model topics and expertise in community question answering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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With the fast development of web 2.0, user-centric publishing and knowledge management platforms, such as Wiki, Blogs, and Q & A systems attract a large number of users. Given the availability of the huge amount of meaningful user generated content, incremental model based recommendation techniques can be employed to improve users' experience using automatic recommendations. In this paper, we propose an incremental recommendation algorithm based on Probabilistic Latent Semantic Analysis (PLSA). The proposed algorithm can consider not only the users' long-term and short-term interests, but also users' negative and positive feedback. We compare the proposed method with several baseline methods using a real-world Question & Answer website called Wenda. Experiments demonstrate both the effectiveness and the efficiency of the proposed methods.