Sparsity Reduction in Collaborative Recommendation: A Case-Based Approach

An approach to addressing the sparsity problem is presented by describing and evaluating how implicit similarity knowledge can be discovered and exploited using data-mining techniques and an approach to recommendation that is inspired by case-based reasoning research.

Abstract

Recommender systems research combines techniques from user modeling and information filtering in order to build search systems that are better able to respond to the preferences of individual users during the search for a particular item or product. Collaborative recommenders leverage the preferences of communities of similar users in order to guide the search for relevant items. The success of collaborative recommendation has always been restrained by the so-called sparsity problem, in which a lack of available user similarity knowledge ultimately limits the formation of high-quality user communities and has a subsequent impact on recommender accuracy. This article presents an approach to addressing the sparsity problem by describing and evaluating how implicit similarity knowledge can be discovered and exploited using data-mining techniques and an approach to recommendation that is inspired by case-based reasoning research.