Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommende...

In customer relationship management (CRM), online recommender assumes an important role of suggesting the right product or information to the right customer automatically. Hence customers are empowered with the choices that are predicted to be preferred by the system. The underlying technique is often a collaborative filtering (CF) algorithm that harvests both information from similar products ...

We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science...

We acknowledge the fact that situational details can have impact on the trust that a Trustor assigns to some Trustee. Motivated by that, we discuss and formalize functions for determining context-aware trust. A system implementing such functions takes into account the Trustee’s profile realized by what we call quality attributes. Furthermore, the system is aware of some context attributes chara...

Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized ...

Context-aware recommender systems extend traditional recommender systems by adapting their output to users’ specific contextual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). In this paper, we highlight the importance of context simil...

Agent-based recommender systems have been widely employed in the last years to provide informative suggestions to users, showing the advantage of exploiting components like beliefs, goals and trust in the recommendation computation. However, many real-world recommendation scenarios, like the traffic or the health ones, require to represent and reason about spatial and temporal knowledge, consid...

This paper focuses on stability of recommendation algorithms, which measures the consistency of recommender system predictions. Stability is a desired property of recommender systems and has important implications on users' trust and acceptance of recommendations. Prior research has reported that some popular recommendation algorithms suffer from high degree of instability. In this study we pro...

Recommender Systems based on Information Filtering techniques are utilized to an increasing degree in order to provide personalized information, countering information overload. Due to the antagonism of personalization and privacy, however, current Recommender System architectures are not suitable for use with extensive and sensitive user profile data. We propose a novel approach to agentbased ...

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite ...