Most recommender systems usually combine several recommendation methods to enhance the accuracy. Collaborative filtering (CF) is a best-known personalized technique. While temporal association rule-based algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key probl...

In this paper we propose a new approach for improving the personalization of POIs recommender system. Existing context-aware POIs recommender systems usually take into account only peripheral contextual variables. We present Ricochet, an ontology-based system that refines the recommendation results by implementing an inter-POI parameter that we call the “complementarity”. We show how this new p...

Unlike traditional recommendation systems that rely only on the user's preferences, context-aware (CARS) consider contextual information such as (time, weather, and geographical location). These data are used to create more intelligent effective systems. Time is one of most important influential factors affect users’ preferences purchasing behavior. Thus, in this paper, time-aware investigated ...

Context aware product recommender systems are an ongoing field of [1]. Contextual information in this case can for example be the customer’s mood, her age, or place of interaction between the recommender system and the user. The well-established pervasiveness of smartphones even allows to extend a customer's context by location (e.g. [2]) which is easily derived using GPS or WiFi. As soon as th...