Managing uncertainty in group recommending processes

Campos, Luis M. de; Fernández‐Luna, Juan M.; Huete, Juan F.; Rueda-Morales, Miguel A.

doi:10.1007/s11257-008-9061-1

Cited by 45 publications

(33 citation statements)

References 24 publications

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“…The problem of individual-based recommendation has been extensively studied and a number of techniques have been proposed [5,26,21,12,19]. More recently, researchers have started investigating the problem of group recommendation [25,8,35,11,33,30,13,18]. They propose solutions that either create a "pseudo-user" profile for each group, or merge the recommendation lists of individual users at runtime using different group decision strategies, such as average satisfaction, minimum misery, or maximum satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Enhancing group recommendation by incorporating social relationship interactions

Gartrell

Xing

et al. 2010

Proceedings of the 16th ACM International Conference on Supporting Group Work - GROUP '10

154

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Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance.In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

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Section: Introductionmentioning

confidence: 99%

Enhancing group recommendation by incorporating social relationship interactions

Gartrell

Xing

et al. 2010

Proceedings of the 16th ACM International Conference on Supporting Group Work - GROUP '10

154

View full text Add to dashboard Cite

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“…Therefore, a higher value of N u concession determines a higher level of cooperativeness. Given that the UserAgent knows that the preferences in the mediator's proposal are ordered according to their degree of 7 Note that the list used to check whether the requirements of the UserAgent are satisfied is the list accepted t 1 , instead of the new proposal of the NegotiatorAgent, which has not already been consensued by the UserAgents. Table 5 Example of levels of negotiation in the Tourism domain (Movielens dataset).…”

Section: Step 1 Analysis Of the Negotiatoragent Messagementioning

confidence: 99%

“…There are many remarkable GRS based on the elicitation of preferences; examples include Intrigue [1], Polylens [29], MusicFX [26], Let's Browse [18], The Collaborative Advisory Travel System, CATS [27] and GRSK [9]. A description of these GRSs joint with a classification upon different features can be found in [5,7,9,25].…”

Section: Introductionmentioning

confidence: 99%

A negotiation framework for heterogeneous group recommendation

García

Sebastiá

2014

Expert Systems with Applications

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Over the last years, some remarkable recommender systems for group of users have been developed. When using most of these systems, each group member communicates his/her preferences to the system, which obtains a group profile as the result of an equal weighting of the individual preferences. This way, no member is particularly dissatisfied with the recommendations. However, this is not a realistic situation, given that not all the members in a group act in the same manner. This paper deals with the problem of recommendation for a group of users, where, besides his/her own preferences, each user may have different expectations about the result of the recommendation and may exhibit a different behaviour with respect to the other group members. Moreover, all this information is private and may be revealed under certain circumstances. In this context, we have opted for building a multi-agent system, where an agent acts on behalf of one group member. We have implemented a UserAgent that can be configured in order to exhibit the behaviour desired by the corresponding user. Then, different UserAgents negotiate with the aim of building a group profile that satisfies their particular minimum requirements, while preserving some privacy. Moreover, we have designed a NegotiatorAgent, which governs the negotiation and may act as a mediator in order to facilitate the agreement. Finally, we have performed some experiments that show that this mechanism is able to give a response in this heterogeneous environment.

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“…As for related work in the GRS domain, most systems assume knowledge of the users' preferences on the items [7] while some studies suggest effective methods to acquire them [5]. To our knowledge, only de Campos [3] is the deals with uncertainties in GRS and estimates the user preferences using Bayesian Netowrks. HeThey then computes an estimated recommended item while we compute a definite winner, i.e.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies [3] deal with this challenge by computing the probabilities of the user preferences on candidate items and thus predict a probable winning item. We, on the other hand, assume that the distribution of the preferences of the users Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Iterative voting under uncertainty for group recommender systems

Dery

Kalech

Rokach

et al. 2010

Proceedings of the Fourth ACM Conference on Recommender Systems

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Group Recommendation Systems (GRS) aim at recommending items that are relevant for the joint interest of a group of users. Voting mechanisms assume that users rate all items in order to identify an item that suits the preferences of all group members. This assumption is not feasible in sparse rating scenarios which are common in the recommender systems domain. In this paper we examine an application of voting theory to GRS. We propose a method to accurately determine the winning item while using a minimal set of the group members ratings, assuming that the recommender system has probabilistic knowledge about the distribution of users' ratings of items in the system. Since computing the optimal minimal set of ratings is computationally intractable, we propose two heuristic algorithms that proceed iteratively that aiming atto minimizing the number of required ratings, until identifying a "winning item". Experiments with the Netflix data show that the proposed algorithms reduce the required number of ratings for identifying the "winning item" by more than 50%.

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Managing uncertainty in group recommending processes

Cited by 45 publications

References 24 publications

Enhancing group recommendation by incorporating social relationship interactions

Enhancing group recommendation by incorporating social relationship interactions

A negotiation framework for heterogeneous group recommendation

Iterative voting under uncertainty for group recommender systems

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