in recent years, collaborative filtering (cf) methods are important and widely accepted techniques are available for recommender systems. one of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. however, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. with the dev...

Development of Web 2.0 enabled users to share information online, which results into an exponential growth of world wide web data. This leads to the so-called information overload problem. Recommender Systems (RS) are intelligent systems, helping on-line users to overcome information overload by providing customized recommendations on various items. In real world, people are willing to take adv...

Recommender systems are an effective solution to the information overload problem, specially in the online world where we are constantly faced with inordinately many choices. These systems try to find the items such as books or movies that match best with users’ preferences. Based on the different approaches to finding the items of interests to users, we can classify the recommender systems int...

In this paper we propose a recommender system that suggests the best moment to end a negotiation. The recommendation is made from a trust evaluation of every agent in the negotiation based on their past negotiation experiences. For this, we introduce the Trust Aware Negotiation Dissolution algorithm.