This work describes a multiagent recommender system where agents work on behalf of members of a group of customers, trying to reach the best recommendation for the whole group. The goal is to model the group recommendation as a distributed constraint optimization problem, taking customer preferences into account and searching for the best so- lution. Experimental results show that this approach can be sucessfully applied to propose recommendations to a group of users
There are increasingly many personalization services in ubiquitous computing environments that invol...
In this paper, we revisit the group recommendation problem, by taking into consideration the informa...
In daily life groups are formed naturally, such as watching a movie with friends, or going out for d...
Classic group recommender systems focus on providing suggestions for a fixed group of people. Our wo...
This paper describes a multiagent recommender ap-proach based on the collaboration of multiple agent...
Systems that recommend items to a group of two or more users raise a number of challenging issues th...
Abstract—We examine the problem of enabling the flexibility of updating one’s preferences in group r...
Most research on group recommender systems relies on the assumption that individuals have conflictin...
Group modeling is the process that combines multiple user models into a single model. In group recom...
In this paper, we propose a unified framework and an algorithm for the problem of group recommendati...
Recommender systems have proven their effectiveness in supporting personalised purchasing decisions ...
International audienceIn recent years, recommender systems have achieved a great success. Popular si...
Recommendation techniques have proven their usefulness as a tool to cope with the information overlo...
Recommender-systems has been a significant research direction in both literature and practice. The c...
In this paper we consider the research challenges of generating a set of recommendations that will s...
There are increasingly many personalization services in ubiquitous computing environments that invol...
In this paper, we revisit the group recommendation problem, by taking into consideration the informa...
In daily life groups are formed naturally, such as watching a movie with friends, or going out for d...
Classic group recommender systems focus on providing suggestions for a fixed group of people. Our wo...
This paper describes a multiagent recommender ap-proach based on the collaboration of multiple agent...
Systems that recommend items to a group of two or more users raise a number of challenging issues th...
Abstract—We examine the problem of enabling the flexibility of updating one’s preferences in group r...
Most research on group recommender systems relies on the assumption that individuals have conflictin...
Group modeling is the process that combines multiple user models into a single model. In group recom...
In this paper, we propose a unified framework and an algorithm for the problem of group recommendati...
Recommender systems have proven their effectiveness in supporting personalised purchasing decisions ...
International audienceIn recent years, recommender systems have achieved a great success. Popular si...
Recommendation techniques have proven their usefulness as a tool to cope with the information overlo...
Recommender-systems has been a significant research direction in both literature and practice. The c...
In this paper we consider the research challenges of generating a set of recommendations that will s...
There are increasingly many personalization services in ubiquitous computing environments that invol...
In this paper, we revisit the group recommendation problem, by taking into consideration the informa...
In daily life groups are formed naturally, such as watching a movie with friends, or going out for d...