TRAVEL INTEREST POINT RECOMMENDATION ALGORITHM BASED ON COLLABORATIVE FILTERING AND GRAPH CONVOLUTIONAL NEURAL NETWORKS

被引：0
作者：
Pan, Lan [1 ]
Wei, Jiayin [1 ]
Lu, Youjun [1 ]
Feng, Fujian [1 ]
机构：
[1] Guizhou Minzu Univ, Coll Data Sci & Informat Engn, Guiyang 550025, Peoples R China
来源：
COMPUTING AND INFORMATICS | 2024年 / 43卷 / 06期
关键词：
Graph convolutional neural network; image denoising encoder; collaborative filtering; domain aggregation; recommendation algorithm;
D O I：
10.31577/cai_2024_6_1516
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Tourist attraction recommendation algorithms have been developed to meet demand related to tourism, spiritual and cultural pursuits. While many studies have been conducted on such algorithms, problems remain regarding tourist interest point recommendation such as ignoring social information, underutilizing context information, and not capturing node relationships which have limited the recommendation performance and representation capability. This paper proposes an algorithm based on graph convolutional neural networks and collaborative filtering (GCNs-CF) for travel interest point recommendation, using an image denoising encoder (IDE) instead of domain aggregation, to better capture the relationships and features between users and adjacent nodes of travel interest point nodes. An adaptive adjustment of the negative sample gradient size is used to solve the problem of slow convergence of graph convolutional neural network. The experimental results show that the proposed method has a higher recommendation effect than other algorithms.
引用
收藏
页码：1516 / 1538
页数：23
相关论文
共 32 条
  • [1] Rahmani H. A, Aliannejadi M, Ahmadian S, Baratchi M, Afsharchi M, Crestani F., LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation, Information Retrieval Technology (AIRS 2019), Springer, Cham, Lecture Notes in Computer Science, 12004, pp. 66-78, (2020)
  • [2] Zhao P, Luo A, Liu Y, Xu J, Li Z, Zhuang F, Sheng V. S, Zhou X., Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation, IEEE Transactions on Knowledge and Data Engineering, 34, 5, pp. 2512-2524, (2022)
  • [3] Xu H, Wu J, Zhao L., Interest Point Recommendation Based on Multi Feature Representation and Attention Mechanism, International Conference on Power Data Science (ICPDS), pp. 14-17, (2020)
  • [4] Xing S, Liu F, Wang Q, Zhao X, Li T., Content-Aware Point-of-Interest Recommendation Based on Convolutional Neural Network, Applied Intelligence, 49, 3, pp. 858-871, (2019)
  • [5] Zhai F, Li B., Point of Interest Recommendation Based on Graph Convolutional Neural Network, Journal of Physics: Conference Series, 1883, (2021)
  • [6] Chen X, Xiong K, Zhang Y, Xia L, Yin D, Huang J. X., Neural Feature-Aware Recommendation with Signed Hypergraph Convolutional Network, ACM Transactions on Information Systems (TOIS), 39, 1, (2020)
  • [7] Cai Z, Yuan G, Qiao S, Qu S, Zhang Y, Bing R., FG-CF: Friends-Aware Graph Collaborative Filtering for POI Recommendation, Neurocomputing, 488, pp. 107-119, (2022)
  • [8] Zhang H, Gan M, Sun X., Incorporating Memory-Based Preferences and Point-of-Interest Stickiness Into Recommendations in Location-Based Social Networks, ISPRS International Journal of Geo-Information, 10, 1, (2021)
  • [9] Febre L, Valdiviezo-Diaz P, Reategui R., Collaborative Filtering Enhanced by Demographic Information for Tourist Sites Recommendations, Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-6, (2022)
  • [10] Li D, Gong Z., A Deep Neural Network for Crossing-City POI Recommendations, IEEE Transactions on Knowledge and Data Engineering, 34, 8, pp. 3536-3548, (2022)
1234