POLYLENS A RECOMMENDER SYSTEM FOR GROUPS OF USERS

Konstan John Riedl - In Proceedings of the European Conference on Computer-Supported Cooperative Work. A General Graph-based Model for Recommendation in Event-based Social Networks.

A Survey On Group Recommender Systems Springerlink

We present PolyLens a new collaborative filtering recommender system designed to recommend items for groups of users rather than for individuals.

. This paper presents a signature based point of interest POI group recommendation system which provides personalized recommendations of places such as restaurants for mobile social groups. Using social psychology to motivate contributions to online communities. D Cosley SK Lam I Albert JA Konstan J Riedl.

Group recommender systems GRSs provide recommendations to groups ie they take all individual group member preferences into account and satisfy them optimally with a sequence of items. Recommending New Items to Ephemeral Groups Using Contextual User Influence. Few researchers have considered the behaviour of the users for group recommendation.

In Polylens the groups formed are permanent groups in which 95 of users are satisfied. Watching television tends to be a social activity. In this paper we discuss different strategies for combining individual user models to adapt to.

A Recommender System for Groups of Users. Centrality-based Group Formation in Group Recommender Systems. However despite the fact that many activities are carried out in groups like going to the theater with friends these systems are focused on recommending items for sole users.

A General Graph-based Model for Recommendation in Event-based Social Networks. A Recommender System for Groups of Users. The paper presents a multidimensional MD approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems.

How recommender system interfaces affect users opinions. A group recommender is more appropriate and useful for domains in which several people participate in a single activity as is often the case with movies and restaurants. This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state.

Proceedings of the SIGCHI conference on Human factors in computing systems. So adaptive television needs to adapt to groups of users rather than to individual users. Nowadays recommender systems are present in multiple application domains such as e-commerce digital libraries music streaming services etc.

A Recommender System for Groups of Users - The system is flexible enough to be adapted to different Proceedings of ECSCW 2001 - Bonn Germany 2001 contextas the filtering is done on the basis of the users pp199-218. In this paper we introduce a novel approach providing ad-hoc groups of users who want to watch a movie together with shared on-demand recommendations on mobile devices. Preferences using questionnaires to investigate what are the 6 McCarthy J and Anagnost T.

We present PolyLens a new collaborative filtering recommender system designed to recommend items for groups of users rather than for individuals. In this paper we describe Seta2000 an infrastructure for the development of recommender systems that support personalized interactions with their users and are accessible from dierent types of devices eg desktop computers and mobile phones. A group recommender is more appropriate and useful for domains in which several people participate in a single activity as is often the case with movies and restaurants.

In recent years recommender systems have achieved great success. A recommender system for groups of users by Dan Cosley Joseph A. ECSCW Bonn Germany 2001 199-218.

A Recommender System for Groups of Users. We present the AGReMo system and report on a user study. A group recommender is more appropriate and useful for domains in which several people participate in a single activity as is often the case with movies and restaurants.

We present PolyLens a new collaborative filtering recommender system designed to recommend items for groups of users rather than for individuals. We present PolyLens a new collaborative filtering recommender system designed to recommend items for groups of. A Recommender System for Groups of Users.

2011 Group Recommender Systems. We present PolyLens a new collaborative filtering recommender system designed to recommend items for groups of users rather than for individuals. It proposes a framework called OrdRec that can easily be integrated into existing systems and relatively easily improve their recommendation.

In Proceedings of the European Conference on Computer Supported Cooperative Work 2001. A Recommender System for Groups of Users 2001 BibTeX. Group recommender systems facilitate decision making in groups of users who need to make a choice together.

Centrality-based Group Formation in Group Recommender Systems. In the music domain these systems are especially useful since users often like to listen to new songs and discover new bands. Personalized Item Rating Distributions discusses a method for adding significant statistical analysis to existing recommendation systems.

Popular sites give thousands of recommendations every day. The Seta2000 infrastructure oers a built-in recommendation engine based on a multi-agent. At the same time group music consumption has proliferated in this domain not just.

Polylens 30 is a system for movie group recommendation. A group recommender is more appropriate and useful for domains in which several people participate in a single activity as is often the case with movies and restaurants. A Recommender System for Groups of Users.

Polylens 15 is a movies recommendation system that allows users to create groups and ask for recommendations that are built by aggregating individual. Cosley D Konstan JA Riedl J. Group recommendation which makes recommendations to a group of users instead of individuals has become increasingly important in both the workspace and peoples social activities such as brainstorming sessions for coworkers and social TV for family members or.

A recommender system for groups of user 2001 by M OConnor D Cosley J A Konstan J Riedl. The growth of location-based social networking LBSN like Foursquare in recent years allowed users to explore POIs easily. Recommending New Items to Ephemeral Groups Using Contextual User Influence.

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