Collaborative filtering with CCAM

Meng Lun Wu, Chia Hui Chang, Rui Zhe Liu

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Recommender system has become an important research topic since the high interest of academia and industry. As a branch of recommender systems, collaborative filtering (CF) systems take its roots from sharing opinions with others and have been shown to be very effective for generating high quality recommendations. However, CF often confronts a problem of sparsity which is caused by relevantly less number of ratings against the unknowns that need to be predicted. In this paper, we consider a hybrid approach which combines the content-based approach with collaborative filtering under a unified model called Co-Clustering with Augmented data Matrix (CCAM). CCAM is based on information-theoretic co-clustering but further considers augmented data matrix like user profile and item description. By presenting results on a better error of prediction, we show that our algorithm is more effective in addressing sparsity through optimizing the co-cluster in mutual information loss between multiple tabular data than algorithm with single data and algorithms do not consider mutual information loss or co-clustering in our prediction framework.

原文???core.languages.en_GB???
主出版物標題Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
頁面245-250
頁數6
DOIs
出版狀態已出版 - 2011
事件10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States
持續時間: 18 12月 201121 12月 2011

出版系列

名字Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
2

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???event.eventtypes.event.conference???10th International Conference on Machine Learning and Applications, ICMLA 2011
國家/地區United States
城市Honolulu, HI
期間18/12/1121/12/11

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