Group recommendation using feature space representing behavioral tendency and power balance among members

Seko, Shunichi; Yagi, Takashi; Motegi, Manabu; Muto, Shin-yo

doi:10.1145/2043932.2043953

Cited by 80 publications

(41 citation statements)

References 18 publications

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“…In (Gartrell et al, 2010), the Authors propose a dissimilarity measure that we used in our case studies, while an approach that provides group recommendations with explicit relationships within a family is proposed in (Berkovsky et al, 2009). Finally, approaches for large groups or small, but static, groups, as in (Vildjiounaite et al, 2008), are not applicable in our case since our groups do not have a history of common activities (as in (Seko et al, 2011)) and have to reach an agreement on a small set of items (i.e., on the activities to perform).…”

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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Section: Background and Related Workmentioning

confidence: 99%

Dominance Weighted Social Choice Functions for Group Recommendations

Rossi

Barile

Caso

2015

ADCAIJ

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“…For a group G ⊆ U , a trivial way of computing the score is to aggregate scores of individual members [9,7]. We propose a novel method of virtual user strategy.…”

Section: Precedence Probabilitiesmentioning

confidence: 99%

Precedence Mining in Group Recommender Systems

Kagita

Padmanabhan

Pujari

2013

Lecture Notes in Computer Science

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Abstract. We extend the Precedence mining model for personal recommendation as outlined in Parameswaran et.al.,[6] in three different ways. Firstly, we show how precedence mining model can be used for recommending items of interest to a group of users (group recommendation) and compare and contrast our model with traditional group recommendation models like collaborative and Hybrid. Secondly, we extend the precedence mining model to incorporate ratings for items and experimental results show that the goodness of recommendation is improved. The third extension is related to the issue of new items being ignored which is a fundamental problem plaguing collaborative and precedence mining algorithms. When recommendations are based on other users interests (like in Collaborative recommender systems) the possibility of not recommending a new item which has not been consumed by many of the users is high though the new item may be of interest to the target user. We outline two models, Vector precedence-mining and Hybrid precedence-mining that addresses this issue.

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“…Of the different recommendation strategies as outlined above two of them stand out: (1) Content-based and (2) Collaborative Filtering. The content-based filtering approach [18,24,26,27] creates a profile for each user/product to characterize its nature. Content-based strategies require gathering external information that might not be available or easy to collect.…”

Section: Introductionmentioning

confidence: 99%

“…The virtual user is assumed to possess behavior that is exhibited by each and every member of the group i.e., the common behavior of the group. From the literature it can be found that virtual user strategy assumes that the history of all group members is the same [27]. In other words, the virtual user profile is generated from individual members profiles by making use of the common items.…”

Section: Introductionmentioning

confidence: 99%