A negotiation framework for heterogeneous group recommendation

García, Inma Mendoza; Sebastiá, Laura

doi:10.1016/j.eswa.2013.07.111

Cited by 29 publications

(15 citation statements)

References 26 publications

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“…They use the information stored in social networks to elicit social factors following two approaches: the cognitive modeling approach and the social approach. Garcia and Sebastia (2014) argue that the aggregation of user preferences to create a group is not a realistic situation, because not all the members in a group act in the same manner. They have opted for building a multi-agent system, where an agent acts on behalf of one group member.…”

Section: Group Recommendationsmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items.Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. VIII IX ABSTRACTThe importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things.As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In thisPhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation.I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present.The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets. The kinds of filtering most used at the beginning of the RS (collaborative, content-based and demographic) were described in Pazzani (1999). Breese,

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Section: Group Recommendationsmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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“…The Negotiation protocol is different according to the cardinality of the group. A similar approach is used in (Garcia et al, 2009) and (Garcia and Sebastia, 2014). In this case, there are agents that act on behalf of group's members, and the recommendations are obtained as the result of the negotiation too.…”

Section: Background and Related Workmentioning

confidence: 99%

Dominance Weighted Social Choice Functions for Group Recommendations

Rossi

Barile

Caso

2015

ADCAIJ

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In travel domains, decision support systems provide support to tourists in the planning of their vacation. In particular, when the number of possible Points of Interest (POI) to visit is large, the system should help tourists providing recommendations on the POI that could be more interesting for them. Since traveling is, usually, an activity that involves small groups of people, the system should take simultaneously into account the preferences of each group's member. At the same time, it also should model possible intra-group relationships, which can have an impact in the group decision-making process. In this paper, we model this problem as a multi-agent aggregation of preferences by using weighted social choice functions, whereas such weights are automatically evaluated by analyzing the interactions of the group's members on Online Social Networks.

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“…The approach has only been tested with simulations involving two agents while we will test our approach on bigger groups of users. In (Garcia, et al, 2009) an agent-based negotiation schema that uses alternating offers is developed, in which agents negotiate the preferences of the whole group. The authors of (Sebastiá, et al, 2011) propose a system named e-Tourism that is able to produce recommendations for both individuals and groups, but the downside of this system is that for producing the latter it makes use of aggregation techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Some examples are the systems proposed in (Blanco-Fernandez, et al, 2004), (Skocir, et al, 2012), (Bekkerman, et al, 2006), (Garcia, et al, 2009), among others. However, particularly for group recommendation, there are not many systems and from those which do use MAS for generating group recommendations only one of them (Garcia, et al, 2009) avoids the use of aggregation techniques in the recommendation process.…”

Section: Introductionmentioning

confidence: 99%