A Hidden Markov Model for Collaborative Filtering

Abstract:     
We present a hidden Markov model for collaborative filtering of implicit ratings when the ratings have been generated by a set of changing user preferences. Most of the works in the collaborative filtering and recommender systems literature have been developed under the assumption that user preference is a static pattern. However, we show by analyzing a dataset on employees’ blog reading behaviors that users’ reading behaviors do change over time. We model the unobserved user preference as a Hidden Markov sequence. The observation that users read variable numbers of blog articles in each time period and choose different types of articles to read, requires a novel observation model. We use a Negative Binomial mixture of Multinomials to model such observations. This allows us to identify stable global preferences of users towards the items in the dataset and allows us to track the users through these preferences. We compare the algorithm with a number of static algorithms and a recently proposed dynamic collaborative filtering algorithm and find that the proposed HMM based collaborative filter outperforms the other algorithms.

Number of Pages in PDF File: 33

Keywords: Recommender Systems, Collaborative Filtering, HMM, Implicit Rating

Accepted Paper Series