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- W4313012982 abstract "With the increasing number of Web services with similar functions on the Internet, traditional collaborative filtering service recommendation methods may encounter problems such as data sparseness, cold start, and poor scalability. To solve the above problems, this project proposes a new Web service recommendation method based on the decomposition machine model. The method decomposes the user trust relationship matrix and the product rating matrix while adding the geographic location information of the service, and transforms the correlation matrix of the calculated user feature vector and item feature vector into the same latent factor space by means of a decomposition machine. Optimize training model parameters to provide users with accurate prediction scores. The ultimate purpose of QoS prediction is to recommend high-quality services to users, improve the efficiency of users' discovery and selection of high-quality services, and ultimately promote the utilization of network Web services and promote service providers to release higher-quality services. Scientific significance, but also has better application value." @default.
- W4313012982 created "2023-01-05" @default.
- W4313012982 date "2022-01-01" @default.
- W4313012982 modified "2023-09-26" @default.
- W4313012982 title "An Web Service Recommendation Method Based on Location and Decomposition Machine Model" @default.
- W4313012982 doi "https://doi.org/10.23977/autml.2022.030207" @default.
- W4313012982 hasPublicationYear "2022" @default.
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