Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model
In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/9292874 |
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| _version_ | 1832567721974300672 |
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| author | Meng Shi |
| author_facet | Meng Shi |
| author_sort | Meng Shi |
| collection | DOAJ |
| description | In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF model. In the problem of cross-domain recommendation, this article clusters users and items in each data domain with hidden scoring patterns and learns common scoring patterns that can be shared between different data domains to deal with the data sparse problem that often occurs in recommender systems. The experimental research results show that cross-domain recommendation can indeed use the implicit semantics or topics between domains to share information and knowledge, thereby improving the accuracy of recommendation. |
| format | Article |
| id | doaj-art-489cbcde690a494faaff000881948bf1 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-489cbcde690a494faaff000881948bf12025-02-03T01:00:45ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/9292874Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor ModelMeng Shi0Changzhi Vocational and Technical CollegeIn order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF model. In the problem of cross-domain recommendation, this article clusters users and items in each data domain with hidden scoring patterns and learns common scoring patterns that can be shared between different data domains to deal with the data sparse problem that often occurs in recommender systems. The experimental research results show that cross-domain recommendation can indeed use the implicit semantics or topics between domains to share information and knowledge, thereby improving the accuracy of recommendation.http://dx.doi.org/10.1155/2022/9292874 |
| spellingShingle | Meng Shi Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model Advances in Multimedia |
| title | Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model |
| title_full | Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model |
| title_fullStr | Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model |
| title_full_unstemmed | Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model |
| title_short | Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model |
| title_sort | personalized recommendation of online shopping products based on online fast learning through latent factor model |
| url | http://dx.doi.org/10.1155/2022/9292874 |
| work_keys_str_mv | AT mengshi personalizedrecommendationofonlineshoppingproductsbasedononlinefastlearningthroughlatentfactormodel |