Heterogeneous recommendations

Recommenders, as widely implemented nowadays by major e-commerce players like Netflix or Amazon, use collaborative filtering to suggest the most relevant items to their users. Clearly, the effectiveness of recommenders depends on the data they can exploit, i.e., the feedback of users conveying their preferences, typically based on their past ratings. As of today, most recommenders are homogeneous in the sense that they utilize one specific application at a time. In short, Alice will only get recommended a movie if she has been rating movies. But what if she has been only rating books and would like to get recommendations for a movie? Clearly, the multiplicity of web applications is calling for heterogeneous recommenders that could utilize ratings in one application to provide recommendations in another one. This paper presents X-M ap , a heterogeneous recommender. X-M ap leverages meta-paths between heterogeneous items over several application domains, based on users who rated across these domains. These meta-paths are then used in X-M ap to generate, for every user, a profile ( AlterEgo ) in a domain where the user might not have rated any item yet. Not surprisingly, leveraging meta-paths poses non-trivial issues of (a) meta-path-based inter-item similarity , in order to enable accurate predictions, (b) scalability , given the amount of computation required, as well as (c) privacy , given the need to aggregate information across multiple applications. We show in this paper how X-M ap addresses the above-mentioned issues to achieve accuracy, scalability and differential privacy. In short, X-M ap weights the meta-paths based on several factors to compute inter-item similarities, and ensures scalability through a layer-based pruning technique. X-M ap guarantees differential privacy using an exponential scheme that leverages the meta-path-based similarities while determining the probability of item selection to construct the AlterEgos. We present an exhaustive experimental evaluation of X-M ap using real traces from Amazon. We show that, in terms of accuracy, X-M ap outperforms alternative heterogeneous recommenders and, in terms of throughput, X-M ap achieves a linear speedup with an increasing number of machines.