Feature-based recommendation system

Eui Hong Han, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations

Abstract

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems - a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based and model-based collaborative filtering are the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. The basic assumption in these algorithms is that there are sufficient historical data for measuring similarity between products or users. However, this assumption does not hold in various application domains such as electronics retail, home shopping network, on-line retail where new products are introduced and existing products disappear from the catalog. Another such application domains is home improvement retail industry where a lot of products (such as window treatments, bathroom, kitchen or deck) are custom made. Each product is unique and there are very little duplicate products. In this domain, the probability of the same exact two products bought together is close to zero. In this paper, we discuss the challenges of providing recommendation in the domains where no sufficient historical data exist for measuring similarity between products or users, We present feature-based recommendation algorithms that overcome the limitations of the existing top-N recommendation algorithms. The experimental evaluation of the proposed algorithms in the real life data sets shows a great promise. The pilot project deploying the proposed feature-based recommendation algorithms in the on-line retail web site shows 75% increase in the recommendation revenue for the first 2 month period.

Original languageEnglish (US)
Title of host publicationCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
Pages446-452
Number of pages7
DOIs
StatePublished - 2005
EventCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management - Bremen, Germany
Duration: Oct 31 2005Nov 5 2005

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

OtherCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
CountryGermany
CityBremen
Period10/31/0511/5/05

Keywords

  • Collaborative filtering
  • E-Commerce
  • Product Features
  • Recommender systems
  • Web Retailer

Fingerprint Dive into the research topics of 'Feature-based recommendation system'. Together they form a unique fingerprint.

Cite this