A Robust Collaborative Filtering Algorithm Using Ordered Logistic Regression

The Internet offers tremendous opportunities for information sharing and content distribution. However, without proper filtering and selection, the large amount of information may likely swarm the users rather than benefit them. Collaborative filtering is a technique for extracting useful information from the large information pool generated by interconnected online communities. In this paper, we develop a probabilistic collaborative filtering algorithm, which is based on ordered logistic regression and takes into account both similarities among the users and similarities among the items. We make inference with maximum likelihood and Bayesian frameworks, and propose a Markov Chain Monte Carlo based Expectation Maximization algorithm to optimize model parameters. The power of our proposed algorithm is its extensibility. We show that it can incorporate content and contextual information. More importantly, it can be easily extended to include the trustworthiness of users, thus being more robust to malicious data manipulation. The experimental results on a real world data set show that our proposed algorithm with the trust extension is robust under different types of attacks in recommendation systems.