2022
DOI: 10.1371/journal.pone.0266103
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Abstract: Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybr… Show more

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Cited by 3 publications
(3 citation statements)
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“…Ali et al use fuzzy C-means clustering to assign a certain degree of membership to each user for each group, and then form groups using Pearson similarity. The method allows users to be members of multiple groups, showing a certain degree of generalization [13] .…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Ali et al use fuzzy C-means clustering to assign a certain degree of membership to each user for each group, and then form groups using Pearson similarity. The method allows users to be members of multiple groups, showing a certain degree of generalization [13] .…”
Section: Related Workmentioning
confidence: 99%
“…∂ HTGF [13] : HTGF is a two-stage group recommendation framework that utilizes DNN for model training based on users' explicit preferences. It uses PCC (Pearson Correlation Coefficient) and FCM (Fuzzy C-Means) for group division, allowing users to belong to multiple groups.…”
Section: Datasets and Baselinesmentioning
confidence: 99%
“…To solve the problem that GNNs are difficult to capture the implicit information in the interaction direction dimension, Chang et al [ 34 ] propose a novel graph neural network for reciprocal recommendation. Since a similar group of users may also have some different interests, Ali et al [ 35 ] used Pearson similarity to assign users to multiple interest groups, and combined group information and other information for recommendation, which improved the recommendation effect. Since customer reviews play an important role in recommendation decisions, Karthik et al [ 36 ] proposed a fuzzy recommendation system based on sentiment analysis and customer interests.…”
Section: Related Workmentioning
confidence: 99%