Fast Item-Based Collaborative Filtering

David Ben Shimon, Lior Rokach, Bracha Shapira, Guy Shani


Item-based Collaborative Filtering (CF) models offer good recommendations with low latency. Still, constructing such models is often slow, requiring the comparison of all item pairs, and then caching for each item the list of most similar items. In this paper we suggest methods for reducing the number of item pairs comparisons, through simple clustering, where similar items tend to be in the same cluster. We propose two methods, one that uses Locality Sensitive Hashing (LSH), and another that uses the item consumption cardinality. We evaluate the two methods demonstrating the cardinality based method reduce the computation time dramatically without damage the accuracy.


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Paper Citation

in Harvard Style

Ben Shimon D., Rokach L., Shapira B. and Shani G. (2015). Fast Item-Based Collaborative Filtering . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 457-463. DOI: 10.5220/0005227104570463

in Bibtex Style

author={David Ben Shimon and Lior Rokach and Bracha Shapira and Guy Shani},
title={Fast Item-Based Collaborative Filtering},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Fast Item-Based Collaborative Filtering
SN - 978-989-758-074-1
AU - Ben Shimon D.
AU - Rokach L.
AU - Shapira B.
AU - Shani G.
PY - 2015
SP - 457
EP - 463
DO - 10.5220/0005227104570463