A study on the role of uninterested items in group recommendations

Kumar, C. Daniel Nesa; Chowdary, C. Ravindranath

doi:10.1007/s10660-021-09526-4

Cited by 4 publications

(1 citation statement)

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“…In [7], localitysensitive hashing is used on the signature matrix generated by the MinHash technique to determine similar users, thereby accurately detecting groups. At the same time, Kumar et al also propose using the Louvain algorithm, particle swarm optimization based K-means clustering, and Gaussian Mixture Model techniques to obtain a less coarse-grained cluster [8] . Wu et al propose an LSGDM method that uses incomplete preference information to develop the Decision Makers (DMs) similarity network and then uses the community detection model Louvain to divide a large number of DMs into several clusters [9] .…”

Section: Related Workmentioning

confidence: 99%

BSGD: a behavior and social based group discovery method

Han,

Wang,

Zhang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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With the rise of social networks and online communities, group activities become increasingly common, which promotes the development of group recommendations. As the first step of group recommendation, group discovery aims to identify potential groups from users. Current group discovery methods often fail to consider behavior preferences and social connections simultaneously and may not be trained in a group-discovery-oriented manner. To address the above problems, we propose a novel group discovery method called BSGD based on users' behaviors and socialization, which introduces three tasks and implements group-discovery-oriented end-to-end training through multi-task joint training. The experiment results show that the proposed method has an excellent performance in both group discovery and gains in group recommendation on two real-world datasets.

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Section: Related Workmentioning

confidence: 99%