| Summary: | In this work we investigate the problem of making personalized recommendations by creating models for predicting user-item rating, such as in movie recommendations. The study is based on the Movielens data set which has ratings on an ordinal scale. In the past, partly due to motivation gained by the Netflix challenge, researchers have constructed models that make point predictions to minimize the root mean square error (RMSE) on test sets, typically by learning latent user and movie feature structure. In such models, the difference between ratings of 2 and 3 stars is the same as the difference between ratings of 4 and 5 stars, etc., which is a strong prior assumption. We construct probabilistic models which also learn latent user and movie feature structure but do not make this assumption. These models interpret the ratings as categories (nominal and ordinal) and return a probability distribution over the ratings for each user-movie pair instead of making a point prediction. We evaluate and compare our models with other models for making personalized recommendations for the top-n task and comparing the precision vs
recall, receiver operating characteristic and cost curves. Our results show that our ordinal data model performs better than a nominal data model, a state-of-the-art point prediction model, and other baselines. === Science, Faculty of === Computer Science, Department of === Graduate
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