User Model User-Adap Inter(2009)19: 207-242 DOI10.1007/s11257-008-9061-1 ORIGINAL PAPER Managing uncertainty in group recommending Luis M. de Campos. Juan M. Fernandez.Luna Juan F Huete. Miguel A Rueda- Morales Received: 25 February 2008/ Accepted in revised form 26 October 2008/ Published online: 21 November 2008 O Springer Science+Business Media B V. 2008 Abstract While the problem of building recommender systems has attracted con siderable attention in recent years, most recommender systems are designed for rec- ommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group rec ommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender systen where group ratings are computed from the past voting patterns of other users with Probabilistic hical models L M. de Campos.J M. Fernandez-Luna.J F Huete(). M. A. Rueda-Moral Department of Computer Science and Artificial Intelligence, University of Granada, 8071 Granada, Spain e-mail: Ici(@desai.ugres e-mail: jmfluna@desai. ugres M. A. Rueda- Morales e-mail: mrueda (@desai. ugresUser Model User-Adap Inter (2009) 19:207–242 DOI 10.1007/s11257-008-9061-1 ORIGINAL PAPER Managing uncertainty in group recommending processes Luis M. de Campos · Juan M. Fernández-Luna · Juan F. Huete · Miguel A. Rueda-Morales Received: 25 February 2008 / Accepted in revised form : 26 October 2008 / Published online: 21 November 2008 © Springer Science+Business Media B.V. 2008 Abstract While the problem of building recommender systems has attracted con￾siderable attention in recent years, most recommender systems are designed for rec￾ommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group rec￾ommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes. Keywords Group recommending · Management of uncertainty · Probabilistic graphical models L. M. de Campos · J. M. Fernández-Luna · J. F. Huete (B) · M. A. Rueda-Morales Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain e-mail: jhg@decsai.ugr.es L. M. de Campos e-mail: lci@decsai.ugr.es J. M. Fernández-Luna e-mail: jmfluna@decsai.ugr.es M. A. Rueda-Morales e-mail: mrueda@decsai.ugr.es 123