Analysing Compression Techniques for In-Memory Collaborative Filtering

Vargas, S., Macdonald, C. and Ounis, I. (2015) Analysing Compression Techniques for In-Memory Collaborative Filtering. In: RecSys 2015 Poster Proceedings, Vienna, Austria, 16-20 Sep 2015,

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Publisher's URL: http://recsys.acm.org/recsys15/

Abstract

Following the recent trend of in-memory data processing, it is a usual practice to maintain collaborative filtering data in the main memory when generating recommendations in academic and industrial recommender systems. In this paper, we study the impact of integer compression techniques for in-memory collaborative filtering data in terms of space and time efficiency. Our results provide relevant observations about when and how to compress collaborative filtering data. First, we observe that, depending on the memory constraints, compression techniques may speed up or slow down the performance of state-of-the art collaborative filtering algorithms. Second, after comparing different compression techniques, we find the Frame of Reference (FOR) technique to be the best option in terms of space and time efficiency under different memory constraints.

Item Type:Conference Proceedings
Keywords:Recommender Systems, Collaborative Filtering, Index Compression
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Vargas, Mr Saul
Authors: Vargas, S., Macdonald, C., and Ounis, I.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Terrier Team

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