Contextual preference mining for user profile construction

The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying properties of soundness and conciseness. After proving that the problem is NP-complete, we propose a resolution in 2 phases. The first phase extracts all individual user preferences by means of contextual preference rules. The second phase builds the user profile starting from this collection of rules using a greedy method. To assess the quality of user profiles, we propose three ranking techniques benefiting from these profiles that enable us to rank objects according to user preferences. We evaluate the efficacy of our three ranking strategies and compare them with a well-known ranking method (SVMRank). The evaluation is carried out through an extensive set of experiments executed on a real-world database of user preferences about movies.