Dynamic Group Recommendation Algorithm Based on Member Activity Level

The rapid development of social networks has led to an increased desire for group entertainment consumption, making the study of group recommender systems a hotspot. Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results. To this end, a dynamic group recommendation algorithm based on the activity level of members is proposed. Firstly, the algorithm predicts the unknown preferences of members using a time-series-oriented rating prediction model. Secondly, considering the dynamic change of member activity level, the group profile is generated by designing a sliding time window to investigate the recent activity level of each member in the group at the recommended moment, and preference is aggregated based on the recent activity level of members. Finally, the group recommendations are generated based on the group profile. The experimental results show that the algorithm in this paper achieves a better recommendation result.

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Group Recommender Systems – An Evolutionary Approach Based on Multi-expert System for Consensus

Journal of Intelligent Systems ◽

10.1515/jisys-2018-0081 ◽

2018 ◽

Vol 29 (1) ◽

pp. 1092-1108 ◽

Cited By ~ 2

Author(s):

Ritu Meena ◽

Sonajharia Minz

Keyword(s):

Recommender Systems ◽

Group Recommendation ◽

Group Recommender Systems ◽

Maximum Utility ◽

Group Members ◽

Two Phases ◽

The Individual ◽

Multiple Experts ◽

Group Profile ◽

Group Recommender

Abstract Recommender systems have focused on algorithms for a recommendation for individuals. However, in many domains, it may be recommending an item, for example, movies, restaurants etc. for a group of persons for which some remarkable group recommender systems (GRSs) has been developed. GRSs satisfy a group of people optimally by considering the equal weighting of the individual preferences. We have proposed a multi-expert scheme (MES) for group recommendation using genetic algorithm (GA) MES-GRS-GA that depends on consensus techniques to further improve group recommendations. In order to deal with this problem of GRS, we also propose a consensus scheme for GRSs where consensus from multiple experts are brought together to make a single recommended list of items in which each expert represents an individual inside the group. The proposed GA based consensus scheme is modeled as many consensus schemes within two phases. In the consensus phase, we have applied GA to obtain the maximum utility offer for each expert and generated the most appropriate rating for each item in the group. In the recommendation generation phase, again GA has been employed to produce the resulting group profile, i.e. the list of ratings with the minimum sum of distances from the group members. Finally, the results of computational experiments that bear close resemblance to real-world scenarios are presented and compared to baseline GRS techniques that illustrate the superiority of the proposed model.

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A New Approach for Fairness Increment of Consensus-driven Group Recommender Systems Based on Choquet Integral

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.290891 ◽

2022 ◽

Vol 18 (1) ◽

pp. 0-0

Keyword(s):

Recommender Systems ◽

Choquet Integral ◽

Fuzzy Measure ◽

Group Recommendation ◽

Consensus Process ◽

New Approach ◽

Group Recommender Systems ◽

Benchmark Datasets ◽

Group Member ◽

Group Recommender

It has been witnessed in recent years for the rising of Group recommender systems (GRSs) in most e-commerce and tourism applications like Booking.com, Traveloka.com, Amazon, etc. One of the most concerned problems in GRSs is to guarantee the fairness between users in a group so-called the consensus-driven group recommender system. This paper proposes a new flexible alternative that embeds a fuzzy measure to aggregation operators of consensus process to improve fairness of group recommendation and deals with group member interaction. Choquet integral is used to build a fuzzy measure based on group member interactions and to seek a better fairness recommendation. The empirical results on the benchmark datasets show the incremental advances of the proposal for dealing with group member interactions and the issue of fairness in Consensus-driven GRS.

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