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UBC Theses and Dissertations

Symmetric collaborative filtering using the noisy sensor model Sharma, Rita

Abstract

Collaborative filtering is the process of making recommendations regarding the potential preference of a user, for example shopping on the Internet, based on the preference ratings of the user and a number of other users for various items. This thesis considers the problem of symmetric collaborative filtering based on explicit ratings. To evaluate the algorithms, we consider only pure collaborative filtering, using given ratings and excluding other information about the people or items. Our approach is to predict an active user's preferences regarding a particular item by using other people's ratings of that item and other items rated by the active user as noisy sensors. The noisy sensor model uses Bayes' theorem to compute the probability distribution for the active user's rating of a new item. We give two variants for learning the noisy sensor model: one, for explicit binary rating data; and the second, for explicit multi-valued rating data. The model for binary rating data is based on Bayesian learning. Its performance motivate us to further explore the use of noisy sensor model for multi-valued rating data. We give two variant models for multi-valued rating data: in one, we learn a linear model of how users rate items; in another, we assume different users rate items identically, but that the accuracy of the sensors must be learned. We compare the two models of multi-valued rating data with stateof- the-art techniques and show how they are significantly better whether a user has rated only two items or many. We report empirical results using the EachMovie database of movie ratings. We also show that by considering the items similarity along with the users similarity the accuracy of the prediction increases.

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