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A mashup (computer industry jargon), in web development, is a web page or web application that uses content from more than one source to create a single new service displayed in a single graphical interface. For example, a user could combine the addresses and photographs of their library branches with a Google map to create a map mashup. The term implies easy, fast integration, frequently using open application programming interfaces (open API) and data sources to produce enriched results that were not necessarily the original reason for producing the raw source data. The term mashup originally comes from British - West Indies slang meaning to be intoxicated, or as a description for something or someone not functioning as intended. In recent English parlance it can refer to music, where people seamlessly combine audio from one song with the vocal track from another—thereby mashing them together to create something new.
A recommendation method for multi-modal service features based on meta-path fusion
PendingCN122262403AImprove robustnessEliminate recommendation biasDigital data information retrievalVersion controlPathPingFeature learning
The application discloses a kind of based on the recommendation method of multi-modal service feature of meta-path fusion, belong to service recommendation and representation learning field.It includes: collection Mashup application and Service service, and constructs historical call matrix and the multi-modal feature of Service service;Meta-path and similarity calculation are used to Service service, respectively obtain meta-path isomorphism and modal isomorphism;Introduce multilayer perceptron and filter, further combined with all isomorphism, contrast loss and fusion modal feature are obtained by graph convolution network;Based on fusion modal feature and historical call matrix, respectively calculate to obtain second contrast loss and BRP loss;Regularization loss is introduced, to minimize all loss sum reverse training all network parameters, obtain the service retrieval table of training completion.The method of the application can accurately fuse the features of multiple modalities of traditional services, significantly enhance the generalization capability in cold start and sparse scenarios.
Owner:ZHEJIANG UNIV +1