Gaussian Process for Recommender Systems

Nowadays, recommender systems are becoming increasingly important because they can filter noisy information and predict users’ preferences. As a result, recommender system has become one of the key technologies for the emerging personalized information services. To these services, when making recommendations, the items’ qualities, items’ correlation, and users’ preferences are all important factors to consider. However, traditional memory-based recommender systems, including the widely used user-oriented and item-oriented collaborative filtering methods, can not take all these information into account. Meanwhile, the model-based methods are often too complex to implement. To that end, in this paper we propose a Gaussian process based recommendation model, which can aggregate all of above factors into a unified system to make more appropriate and accurate recommendations. This model has a solid statistical foundation and is easy to implement. Furthermore, it has few tunable parameters, therefore it is very suitable for a baseline algorithm. The experimental results on the MovieLens data set demonstrate the effectiveness of our method, and it outperforms several state-of-the-art algorithms.