Rate-Based Collaborative Filtering
Date Issued
2009
Date
2009
Author(s)
Wu, Yao-Chuan
Abstract
Item-based collaborative filtering (CF) recommender system is one of famous and well-performed collaborative filtering recommender system. Item-based CF suffers from the problem of various ratings, while users give more different ratings. It also suffers from the problem of insufficient training data. In order to deal with these problems, we propose new methods called rate-based. In the first step, for each user, cluster items with the same rating. Then, build a model for each rating. Finally, make predictions of ratings by calculating the expectation value of models. Through our rate-based methods, predictions are made by utilizing the models of each rating rather than the neighbors of items which are going to be predicted. Our rate-based methods perform great on the million dataset of MovieLens. The experiment results show that our methods outperform the conventional item-based CF with statistically significant.
Subjects
ratings
collaborative filtering
recommender system
model
Type
thesis
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