Rating Knowledge Sharing in Cross-Domain Collaborative Filtering

Cross-domain collaborative filtering (CF) aims to share common rating knowledge across multiple related CF domains to boost the CF performance. In this paper, we view CF domains as a 2-D site-time coordinate system, on which multiple related domains, such as similar recommender sites or successive time-slices, can share group-level rating patterns. We propose a unified framework for cross-domain CF over the site-time coordinate system by sharing group-level rating patterns and imposing user/item dependence across domains. A generative model, say ratings over site-time (ROST), which can generate and predict ratings for multiple related CF domains, is developed as the basic model for the framework. We further introduce cross-domain user/item dependence into ROST and extend it to two real-world cross-domain CF scenarios: 1) ROST (sites) for alleviating rating sparsity in the target domain, where multiple similar sites are viewed as related CF domains and some items in the target domain depend on their correspondences in the related ones; and 2) ROST (time) for modeling user-interest drift over time, where a series of time-slices are viewed as related CF domains and a user at current time-slice depends on herself in the previous time-slice. All these ROST models are instances of the proposed unified framework. The experimental results show that ROST (sites) can effectively alleviate the sparsity problem to improve rating prediction performance and ROST (time) can clearly track and visualize user-interest drift over time.

I. Introduction

Collaborative filtering (CF) has become the most popular technique in real-world recommender systems since it can efficiently handle a large-scale database in a “content-free” manner. The basic idea of CF is memory-based that finds -nearest neighboring users [1] or items [2] based on historical rating data to predict ratings for an active user. Model-based methods were proposed later and most of them also follow the same basic idea, i.e., finding similar users or/and items, but resort to more complicated clustering techniques in various ways, such as latent variable models [3], [4] and low-rank approximations [5], [6]. Thus, the essential problem of CF methods is how to find similar users/items and how to measure similarities between them.