Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Machine Learning
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
An Improved Recommendation Algorithm in Collaborative Filtering
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Collaborative Filtering Methods for Binary Market Basket Data Analysis
AMT '01 Proceedings of the 6th International Computer Science Conference on Active Media Technology
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
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The two of the most famous techniques in collaborative filtering (CF) are the so-called User-Based CF and Item-Based CF. In this paper, we claim that each of them takes only one-directional information from the user-item ratings matrix to generate recommendations. In other words, the former combines user similarities and the latter tries to make a prediction by utilizing item similarities. We can observe the same appearance in the other CF area using binary user-item matrix in which transactions, i.e. purchase (1) or non-purchase (0), are marked. It means that we may use only half of the total information from the given data set. Completing the missing part of usable information we proposed a new prediction method, two-way cooperative CF which takes both vertical and horizontal information, in the ensemble respect. The proposed prediction scheme does not fix its CF technique but associates two predictions, which come from different CF algorithms, by weighted averaging. To decide fair weights the four cases, equivalent case, user-winning case, item-winning case, and prediction-impossible case are categorized by measuring the amount of information which each CF utilizes, or the degree of the reliability of a prediction model. We also embedded bagging in our prediction frame to make more accurate predictions. Numerical experiments showed that the proposed method outperformed in terms of prediction accuracy and robustness to data sparseness based on both ratings and binary user-item matrix.