SHRec: Scalable Holistic Recommendation

Abstract

The problem of recommending items to users is of high practical importance. For instance, many web services try to find relevant recommendations for the users, e.g., finding relevant movies, social-media friends, restaurants, shopping items, etc. The expansion of the Web and the ever-growing number of people who use web services render the problem of recommendation challenging. The Locality Sensitive Hashing (LSH, for short) is the most known scalable technique for nearest-neighbor search in high dimensional data, and hence the LSH is widely used in most industrial recommendation systems. This paper presents an implementation of the LSH using Google's MapReduce engine. We apply the LSH to a real case study at Google, where we recommend for each web-host a set of outlinks based on the outlink similarity amongst the web-hosts. We identify some performance limitations of the LSH that occur due to specific properties in the data, and that become significant when the scale of the data is large. Furthermore, we present SHRec, a novel technique for scalable recommendation that addresses these performance limitations. Based on real deployment of both SHRec and LSH on Google's infrastructure, and using real data of the crawled Web at Google, where a sample host-level graph of 1.5 Billion web-hosts is extracted, we demonstrate that SHRec is more scalable than LSH. In particular, we show that SHRec is one order of magnitude faster than LSH while achieving better recommendation quality.