MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preference
IntroductionWith the booming development of e-commerce, agricultural product recommendation plays an increasingly important role in helping consumers discover and select products. However, the following three problems still exist in the traditional agricultural product recommendation domain: (1) the...
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Frontiers Media S.A.
2025-01-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1525353/full |
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| author | Peishan Li Peishan Li Peishan Li Lutao Gao Lutao Gao Lutao Gao Lilian Zhang Lilian Zhang Lilian Zhang Lin Peng Lin Peng Lin Peng Chunhui Bai Chunhui Bai Chunhui Bai Linnan Yang Linnan Yang Linnan Yang |
| author_facet | Peishan Li Peishan Li Peishan Li Lutao Gao Lutao Gao Lutao Gao Lilian Zhang Lilian Zhang Lilian Zhang Lin Peng Lin Peng Lin Peng Chunhui Bai Chunhui Bai Chunhui Bai Linnan Yang Linnan Yang Linnan Yang |
| author_sort | Peishan Li |
| collection | DOAJ |
| description | IntroductionWith the booming development of e-commerce, agricultural product recommendation plays an increasingly important role in helping consumers discover and select products. However, the following three problems still exist in the traditional agricultural product recommendation domain: (1) the problem of missing modalities made it difficult for consumers to intuitively and comprehensively understood the product information; (2) most of them relied on shallow information about the basic attributes of agricultural products and ignored the deeper associations among the products; (3) they ignored the deeper connections among individual users and the intrinsic associations between the user embedding and the localized user representation in different modalities, which affected the accuracy of user modeling and hindered the final recommendation effect.MethodsTo address these problems, this paper innovatively proposed an agricultural product recommendation algorithm based on LLAVA and user behavioral characteristics, MP-LLaVRec(Modal Preference - Large Language and Vision Recommendation). It consisted of three main components: (1) LLAVA data enhancement, which introduced a multimodal macromodel to improve the understanding of node attributes; (2) agricultural product association relationship fusion, which constructed and improved the complex association network structure among products to ensure that the system can better understand the substitution relationship, complementary relationship, and implied consumption logic among products; (3) user modal preference feature extraction block, which deeply mined the interaction data between consumers and products, and advanced the effective user feature information from the correspondence between global user representations and local modal user representations.Results and DiscussionWe conduct experiments on a real dataset from Amazon's large-scale e-commerce platform to verify the effectiveness of MP-LLAVRec. The experimental results of four metirs, NDCG@10, NDCG@20, Recall@10 and Recall@20, showed that the method has a better performance than the baseline model. |
| format | Article |
| id | doaj-art-77da820de5ff48279a81a418279c62f7 |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Physics |
| spelling | doaj-art-77da820de5ff48279a81a418279c62f72025-01-31T05:10:22ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-01-011310.3389/fphy.2025.15253531525353MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preferencePeishan Li0Peishan Li1Peishan Li2Lutao Gao3Lutao Gao4Lutao Gao5Lilian Zhang6Lilian Zhang7Lilian Zhang8Lin Peng9Lin Peng10Lin Peng11Chunhui Bai12Chunhui Bai13Chunhui Bai14Linnan Yang15Linnan Yang16Linnan Yang17College of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Yunnan Agricultural University, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Yunnan Agricultural University, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Yunnan Agricultural University, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Yunnan Agricultural University, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Yunnan Agricultural University, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Yunnan Agricultural University, Kunming, ChinaIntroductionWith the booming development of e-commerce, agricultural product recommendation plays an increasingly important role in helping consumers discover and select products. However, the following three problems still exist in the traditional agricultural product recommendation domain: (1) the problem of missing modalities made it difficult for consumers to intuitively and comprehensively understood the product information; (2) most of them relied on shallow information about the basic attributes of agricultural products and ignored the deeper associations among the products; (3) they ignored the deeper connections among individual users and the intrinsic associations between the user embedding and the localized user representation in different modalities, which affected the accuracy of user modeling and hindered the final recommendation effect.MethodsTo address these problems, this paper innovatively proposed an agricultural product recommendation algorithm based on LLAVA and user behavioral characteristics, MP-LLaVRec(Modal Preference - Large Language and Vision Recommendation). It consisted of three main components: (1) LLAVA data enhancement, which introduced a multimodal macromodel to improve the understanding of node attributes; (2) agricultural product association relationship fusion, which constructed and improved the complex association network structure among products to ensure that the system can better understand the substitution relationship, complementary relationship, and implied consumption logic among products; (3) user modal preference feature extraction block, which deeply mined the interaction data between consumers and products, and advanced the effective user feature information from the correspondence between global user representations and local modal user representations.Results and DiscussionWe conduct experiments on a real dataset from Amazon's large-scale e-commerce platform to verify the effectiveness of MP-LLAVRec. The experimental results of four metirs, NDCG@10, NDCG@20, Recall@10 and Recall@20, showed that the method has a better performance than the baseline model.https://www.frontiersin.org/articles/10.3389/fphy.2025.1525353/fulldata augmentationuser representationsmultimodal recommendationLLAVAagricultural product recommendation |
| spellingShingle | Peishan Li Peishan Li Peishan Li Lutao Gao Lutao Gao Lutao Gao Lilian Zhang Lilian Zhang Lilian Zhang Lin Peng Lin Peng Lin Peng Chunhui Bai Chunhui Bai Chunhui Bai Linnan Yang Linnan Yang Linnan Yang MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preference Frontiers in Physics data augmentation user representations multimodal recommendation LLAVA agricultural product recommendation |
| title | MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preference |
| title_full | MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preference |
| title_fullStr | MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preference |
| title_full_unstemmed | MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preference |
| title_short | MP-LLAVRec: an agricultural product recommendation algorithm based on LLAVA and user modal preference |
| title_sort | mp llavrec an agricultural product recommendation algorithm based on llava and user modal preference |
| topic | data augmentation user representations multimodal recommendation LLAVA agricultural product recommendation |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1525353/full |
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