This thesis project was done for Ericsson Research in Stockholm, Sweden. The purpose was to evaluate how well an existing algorithm in a recommender system predicts movie ratings and get an indication of how the users perceive the recommendations given by the system. The recommendations are computed with a revised User-based Collaborative Filtering algorithm that calculates trust amongst people...

In this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like kMeans and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust co...

Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve accuracy of predictions, while behavioral aspects of using recommender systems are often overlooked. In this study, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. W...

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...