Context-Aware Recommendations for Mobile Shopping

This paper presents a context-aware mobile shopping recommender system. A critique-based baseline recommender system is enhanced by the integration of context conditions like weather, time, temperature and the user’s company. These context conditions are embedded into the recommendation algorithm via preand post-filtering. A nearest neighbor algorithm, using the concept of an average selection context, calculates how contextually relevant a recommendation is. Out of 20 clothing items from the hybrid recommendation algorithm, context-aware post-filtering searches for the nine best-fitting items. The resulting context-aware recommender system is evaluated in a user study with 100 test participants. The answers of the user study show, that the recommendations were perceived as being better than the recommendations of a non-context aware recommender system.