Personalizing e-Commerce by Semantics-Enhanced Strategies and Time-Aware Recommendations

Abstract

Current e-commerce recommender systems adapt the selection of commercial items suggested to the users as their preferences evolve over time. However, this adaptation process misses the time elapsed since the user has bought an item, which is an essential parameter that affects differently to each purchased product. This results in some useless recommendations, including regularly items that the users are only willing to buy sporadically. In this paper, we explore a new recommendation strategy that offers time-aware suggestions to e-commerce users, by enhancing reasoning techniques from the Semantic Web with item-dependent time functions. This combination leads to suggestions adapted to the particular needs of each user at any given moment.