Movie Recommendation System Using Social Network Analysis and k-Nearest Neighbor

Xinchang, Khamphaphone; Park, Doo-Soon; Vilakone, Phonexay

doi:10.1007/978-981-13-9341-9_104

Cited by 2 publications

(1 citation statement)

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“…Auto Rec and CDAE utilize an autoencoder framework to extract latent vectors for user-item dynamics, predicting user ratings and generating high-quality movie recommendations. In the realm of recommendation systems, Xinchang, Khamphaphone, Phonexay Vilakone, and Doo-Soon Park [30] proposed using social network analysis and collaborative filtering to form the recommendation system, and the authors' method solved the cold-start problem in the traditional approach. Moreover, they applied the community detection method to cluster user similarities and recommend a movie list to a target user based on similar preferences.…”

Section: Advanced-based Recommendation Systemsmentioning

confidence: 99%

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

Siet,

Peng,

Ilkhomjon

et al. 2024

Applied Sciences

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A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data.

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Section: Advanced-based Recommendation Systemsmentioning

confidence: 99%