Learning to Recommend from Positive Evidence

Ingo Schwab (1) , Wolfgang Pohl (1) , Ivan Koychev (1)

1 FIT.MMK - Institute for Applied Information Technology

In recent years, many systems and approaches for recommending information, products or other objects have been developed. In these systems, often machine learning methods that need training input to acquire a user interest profile are used. Such methods typically need positive and negative evidence of the user's interests. To obtain both kinds of evidence, many systems make users rate relevant objects explicitly. Others merely observe the user's behavior, which fairly obviously yields positive evidence; in order to be able to apply the standard learning methods, these systems mostly use heuristics that attempt to find also negative evidence in observed behavior. In this paper, we present several approaches to learning interest profiles from positive evidence only, as it is contained in observed user behavior. Thus, both the problem of interrupting the user for ratings and the problem of somewhat artificially determining negative evidence are avoided. The learning approaches were developed and tested in the context of the Web-based ELFI information system. It is in real use by more than 1000 people. We give a brief sketch of ELFI and describe the experiments we made based on ELFI usage logs to evaluate the different proposed methods.

Mots clés: machine learning; adaptive recommendation interfaces; user modeling

Domaines: Environnements Informatiques pour l'Apprentissage Humain

Ingo Schwab, Wolfgang Pohl, Ivan Koychev. Learning to Recommend from Positive Evidence. International Conference on Intelligent User Interfaces IUI2000, 2000, United States. pp.241-247, ⟨10.1145/325737.325858⟩. ⟨hal-00190352⟩