Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces

Yoram Bachrach, Yehuda Finkelstein, Ran Gilad-Bachrach, Liran Katzir, Noam Koenigstein, Nir Nice, Ulrich Paquet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A prominent approach in collaborative filtering based recommender systems is using dimensionality reduction (matrix factorization) techniques to map users and items into low-dimensional vectors. In such systems, a higher inner product between a user vector and an item vector indicates that the item better suits the user's preference. Traditionally, retrieving the most suitable items is done by scoring and sorting all items. Real world online recommender systems must adhere to strict response-time constraints, so when the number of items is large, scoring all items is intractable. We propose a novel order preserving transformation, mapping the maximum inner product search problem to Euclidean space nearest neighbor search problem. Utilizing this transformation, we study the efficiency of several (approximate) nearest neighbor data structures. Our final solution is based on a novel use of the PCA-Tree data structure in which results are augmented using paths one hamming distance away from the query (neighborhood boosting). The end result is a system which allows approximate matches (items with relatively high inner product, but not necessarily the highest one). We evaluate our techniques on two large-scale recommendation datasets, Xbox Movies and Yahoo Music, and show that this technique allows trading off a slight degradation in the recommendation quality for a significant improvement in the retrieval time.

Original languageEnglish
Title of host publicationRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery
Pages257-264
Number of pages8
ISBN (Electronic)9781450326681
DOIs
StatePublished - 6 Oct 2014
Externally publishedYes
Event8th ACM Conference on Recommender Systems, RecSys 2014 - Foster City, United States
Duration: 6 Oct 201410 Oct 2014

Publication series

NameRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems

Conference

Conference8th ACM Conference on Recommender Systems, RecSys 2014
Country/TerritoryUnited States
CityFoster City
Period6/10/1410/10/14

Keywords

  • Fast retrieval
  • Inner product search
  • Matrix factorization
  • Recommender systems

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