Comparing Two Recommender Algorithms with the Help of Recommendations by Peers

Abstract. Since more and more Web sites, especially sites of retailers, offer automatic recommendation services using Web usage mining, evaluation of recommender algorithms has become increasingly important. In this paper we present a framework for the evaluation of different aspects of recommender systems based on the process of discovering knowledge in databases introduced by Fayyad et al. and we summarize research already done in this area. One aspect identified in the presented evaluation framework is widely neglected when dealing with recommender algorithms. This aspect is to evaluate how useful patterns extracted by recommender algorithms are to support the social process of recommending products to others, a process normally driven by recommendations by peers or experts. To fill this gap for recommender algorithms based on frequent itemsets extracted from usage data we evaluate the usefulness of two algorithms. The first recommender algorithm uses association rules, and the othe...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Andreas Geyer-Schulz, Michael Hahsler
• Keywords: Data Mining, KDD 2002, Presented Evaluation Framework, Recommender Algorithms, Recommender Systems