{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T11:17:12Z","timestamp":1784805432781,"version":"3.55.0"},"reference-count":23,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"Henan High Education Humanities and Social Science Research","award":["2024-ZZJH-463"],"award-info":[{"award-number":["2024-ZZJH-463"]}]},{"name":"Henan provincial key R & D and promotion","award":["232400410281"],"award-info":[{"award-number":["232400410281"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:p> With the rapid growth of the rural tourism industry, traditional tourism recommendation technologies can no longer meet the necessary requirements. To address the issue of rural tourist attraction recommendations, a rural tourist attraction recommendation model is constructed based on a multi-feature fusion graph neural network. First, construct a feature map based on the relationship between tourists\u2019 preferences and tourist attractions, and incorporate the attention mechanism to enhance the model\u2019s learning capabilities. Second, utilize a two-part graph model to extract positive and negative preference features of tourists, and a conversation graph model to extract tourists\u2019 transfer preference features. Finally, various features are utilized to generate suggested content by computing scores for tourists\u2019 travel preferences. To address the problem of recommending tourist groups, suitable features for random group matching are collected and the cosine function is employed to identify users with similar random group features. Finally, the multi-features are merged, and the tourists\u2019 interest preferences are scored to arrive at content recommendations. In the experiment on individualized attraction recommendations, data from the Chengdu area were used to test the proposed model. The accuracy of the model\u2019s recommendations was 0.822 for five recommendations which outperformed the other models. In the experiment for group-based attraction recommendations, this experiment tested the Chengdu dataset. The proposed model achieved the highest accuracy of 0.972 when the group size was 70, outperforming the other two models. Additionally, with regards to different numbers of recommendations, the proposed model\u2019s accuracy was 0.5241, which was the best performance among the three models when the number of recommendations was set to five. The proposed recommendation model performs optimally in suggesting tourist attractions and meets the needs of rural tourism. The research content provides crucial technical references for tourist traveling and rural tourism development. <\/jats:p>","DOI":"10.1142\/s1469026824500275","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T11:16:10Z","timestamp":1731496570000},"source":"Crossref","is-referenced-by-count":2,"title":["Rural Tourist Attractions Recommendation Model Based on Multi-Feature Fusion Graph Neural Networks"],"prefix":"10.1142","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3305-3560","authenticated-orcid":false,"given":"Xiangrong","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Tourism Management, Henan Vocational College of Agriculture, Zhengzhou 451450, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1451-0779","authenticated-orcid":false,"given":"Xueying","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Digital Business and Management, West Yunnan University of Applied Science, Dali 671000, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"issue":"1","key":"S1469026824500275BIB001","first-page":"215","volume":"2","author":"Valentino V. 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