Initial successes in the area of recommender systems have led to considerable early optimism. However as a research community, we are still in the early days of our understanding of recommender systems. Evaluation metrics continue to be refined but we still need to account for the relative contributions of the various knowledge elements that play a part in the recommendation process. In this pa...

Recommender systems are efficient tools that overcome the information overload problem by providing users with the most relevant contents. This is generally done through user’s preferences/ratings acquired from log files of his former sessions. Besides these preferences, taking into account the interaction context of the user will improve the relevancy of recommendation process. In this paper, ...

Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions take next. Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing process-aware recommender system. We propose architecture, namely Write-Protected Dual Cont...

Much research has recently been carried out on the incorporation of trust models into recommender systems. It is generally understood that trust-based recommender systems can help to improve the accuracy of predictions. Moreover they provide greater robustness against profile injection attacks by malicious users. In this paper we analyze these contentions in the context of two trust-based algor...

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to d...

Generating personalized recommendations for new users is particularly challenging, because in this case, the recommender system has little or no user record of previously rated items. Connecting the newcomer to an underlying trust network among the users of the recommender system alleviates this socalled cold start problem. In this paper, we study the effect of guiding the new user through the ...

This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user’s needs based on both the user’s interests and his current context. In order to provide context-aware recommendations, a recommender system has been integrated with a contextaware application platform. We describe how this integration has been accomplished and how users feel about such...

A context-aware recommender system incorporates the knowledge of different contextual factors such as time or weather information to improve item suggestions made to a user. This requires the system to have a large knowledge base for inferring contextual information and enabling accurate and timely recommendations. We present a versatile approach for a context-aware recommender system in the to...

Recommender Systems require specific datasets to evaluate their approach. They do not require the same information: descriptions of users or items or users interactions may be necessary, which is not gathered in today datasets. In this paper, we provide a dataset containing reviews from users on items, trust values between users, items category, categories hierarchy and users expertise on categ...