Authors:
David Ben Shimon
;
Lior Rokach
;
Bracha Shapira
and
Guy Shani
Affiliation:
Ben Gurion University of the Negev, Israel
Keyword(s):
Item-Based, Locality Sensitive Hashing, Collaborative-Filtering, top-N recommendations
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Industrial Applications of AI
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
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.