Recommendations using information from multiple association rules

Abhijeet Ghoshal, Syam Menon, Sumit Sarkar

Research output: Contribution to conferencePaper

Abstract

Personalized recommendations enable firms to effectively target customers with products and services. Such systems are often based on association rules. While there has been considerable work done on mining rules more efficiently, there is very little prior research that examines how to use rules effectively when making recommendations. Traditional association rule-based recommendation systems have relied on identifying one rule from the several eligible ones to make the recommendation. This ignores information from other eligible rules that can potentially improve the recommendation. We propose a method to combine multiple rules when making recommendations. In doing so, we also present an approach to select the best combination of rules from the many that might be available.

Original languageEnglish (US)
Pages13-18
Number of pages6
StatePublished - Jan 1 2009
Externally publishedYes
Event19th Workshop on Information Technologies and Systems, WITS 2009 - Phoenix, AZ, United States
Duration: Dec 14 2009Dec 15 2009

Conference

Conference19th Workshop on Information Technologies and Systems, WITS 2009
CountryUnited States
CityPhoenix, AZ
Period12/14/0912/15/09

Keywords

  • Bayesian estimation
  • Maximum likelihood
  • Mutual information
  • Personalization

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering

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  • Cite this

    Ghoshal, A., Menon, S., & Sarkar, S. (2009). Recommendations using information from multiple association rules. 13-18. Paper presented at 19th Workshop on Information Technologies and Systems, WITS 2009, Phoenix, AZ, United States.