Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity
Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fu...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9998948 |
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| _version_ | 1841561576374534144 |
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| author | Lili Wang Ting Shi Shijin Li |
| author_facet | Lili Wang Ting Shi Shijin Li |
| author_sort | Lili Wang |
| collection | DOAJ |
| description | Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results. |
| format | Article |
| id | doaj-art-a564f16380524167bfa032f203385aa5 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a564f16380524167bfa032f203385aa52025-01-03T01:30:36ZengWileyComplexity1099-05262021-01-01202110.1155/2021/99989489998948Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location SimilarityLili Wang0Ting Shi1Shijin Li2Chongqing Key Laboratory of Spatial Data Mining and Big Data Integration for Ecology and EnvironmentYunnan Business Information Engineering SchoolAcademic Affairs OfficeSince the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.http://dx.doi.org/10.1155/2021/9998948 |
| spellingShingle | Lili Wang Ting Shi Shijin Li Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity Complexity |
| title | Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity |
| title_full | Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity |
| title_fullStr | Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity |
| title_full_unstemmed | Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity |
| title_short | Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity |
| title_sort | research on the application of user recommendation based on the fusion method of spatially complex location similarity |
| url | http://dx.doi.org/10.1155/2021/9998948 |
| work_keys_str_mv | AT liliwang researchontheapplicationofuserrecommendationbasedonthefusionmethodofspatiallycomplexlocationsimilarity AT tingshi researchontheapplicationofuserrecommendationbasedonthefusionmethodofspatiallycomplexlocationsimilarity AT shijinli researchontheapplicationofuserrecommendationbasedonthefusionmethodofspatiallycomplexlocationsimilarity |