Application of Improved K-Means Algorithm in Collaborative Recommendation System

Jing, Hui

doi:10.1155/2022/2213173

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…Moreover, they applied the community detection method to cluster user similarities and recommend a movie list to a target user based on similar preferences. Aiming to develop an improved collaborative movie recommendation system which combines K-means clustering with neural networks, Jing and Hui [31] introduced the hybrid approach, which applies K-means to cluster movies into groups and then trains a neural network model to learn users' preferences based on the clusters to provide more accurate and personalized recommendations, especially for new users with sparse data. Wang, Kai et al [32] presented a novel model that combines k-means clustering with a deep neural network to generate personalized recommendations for users in e-commerce applications, which can effectively solve problems of sparse data and information overload.…”

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%