A hybrid group-based movie recommendation framework with overlapping memberships

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 Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.

BSGD: a behavior and social based group discovery method

“…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] .…”

“…∂ 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.…”

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

“…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.…”

Time Loss Function-based Collaborative Filtering in Movie Recommender System

“…Various recommender systems, including collaborative, content-based, knowledge-based, demographic, utility-based, and hybrid filtering, combine other techniques [7][8][9][10]. Collaborative filtering is among the most well-known, practical, and frequently applied algorithms among the several recommender systems approach [4,11,12].…”