{"id":7576,"date":"2026-06-04T11:11:24","date_gmt":"2026-06-04T03:11:24","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7576"},"modified":"2026-06-04T14:54:02","modified_gmt":"2026-06-04T06:54:02","slug":"jase-202609-32-072","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-072","title":{"rendered":"Deep Attention Network Designed with Cluster for Intelligent Recommendation System"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Dong Liang and Qiang Han<\/i><\/a><\/p>\n\n\n\n

Qiongtai Normal University, Haikou, Hainan 570100, China<\/p>\n<\/div>\n\n\n\n

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Received: March 9, 2026
Accepted: April 4, 2026
Publication Date: June 4, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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AUC results of different pruning probabilities on Last.FM.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.072<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper proposes a deep attention network based on cluster design (ASDCLK), which effectively removes noise and dynamically adjusts the weights of knowledge triplets by introducing a degree sensitive graph structure denoising module and an attention-based knowledge aggregation mechanism, thereby improving recommendation accuracy. The experiment was conducted on public datasets for music recommendation and movie recommendation scenarios, and the results showed that the ASDCLK model outperformed current advanced recommendation methods in CTR prediction and top-K recommendation tasks. Especially on the Last.FM and MovieLens-1M datasets, ASDCLK performs well in AUC and MovieLens-1M, respectively Recall@20 There is a significant improvement in indicators compared to the baseline model. In addition, by adjusting the sampling probability of graph structure denoising, this study found that a moderate sampling probability can effectively remove noise while preserving key interactive information, thereby achieving optimal recommendation performance. The model is evaluated on the MovieLens-1M and Last.FM datasets, comprising thousands of users and interactions, using standard evaluation metrics such as AUC, F1-score, and Recall@K to comprehensively assess recommendation performance.<\/p>\n\n\n\n

Keywords: Intelligent Recommendation System; Graph Neural Network (GNN); Attention Mechanism; Knowledge Graph; Top-K Recommendation<\/em><\/p>\n\n\n\n

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