Towards Intention, Contextual and Social Based Recommender System

This article proposes a recommender system of shopping places for users in mobility with given intentions. It considers the user intention, her context and her location-based social network for providing recommendations using a method based on association rules mining. The context is considered at multiple levels: first in selecting the interesting rules following to the current session of visits of the user; then into a relevance measure containing a geographic measure that considers her current geographic position. Besides the ge-ographic measure, the relevance measure combines a social measure and an in-terest measure. The social measure takes into account the habits of the user’s friends and the interest measure depends on the current intention of the user among predefined intention scenarios. An important aspect of this work is that we propose a framework allowing the personalization of the recommendations by acting only on the relevance measure. This proposition is tested on a real dataset and we show that the use of social and geographic features beyond usage information can improve contextual recommendations.