Mobile Web service recommendation method and collaborative recommendation system based on user behavior analysis

Abstract

The invention discloses a mobile Web service recommendation method and a collaborative recommendation system based on user behavior analysis. The method comprises the steps of first constructing a Web service prediction model, and searching two optimal associated users who are the most similar to the a target user in long-term habit and short-term mood through intelligent terminal side user browsing data and wearable device side user physiological data. Therefore, data of the two optimal associated users are used to enrich a sample library of the target user prediction model, so as to achieve the introduction of a sample enrichment mechanism with the minimized noise. A Web resource storage mechanism based on a feature vector is designed, and according to a model prediction result, accurate recommendation of the mobile Web service is realized. According to the scheme of the invention, through the interactive cooperation between an intelligent terminal and a wearable device, application reconfiguration is performed on the wearable device side physiological data, and a user behavior is analyzed from multiple angles of view and multiple dimensions, so that accurate prediction and recommendation of services are realized, thereby improving user experience.

Description

technical field [0001] The invention relates to a mobile Web service recommendation method and a collaborative recommendation system based on user behavior analysis, and belongs to the technical field of recommendation. Background technique [0002] In response to the explosive growth trend of mobile Internet services, the service recommendation engine, as an effective tool to solve the problems of user information overload and information confusion, can filter interested services for users from massive information by acquiring and predicting the potential preferences of mobile users, and Realize service pre-download, achieve the effect of balancing network load and reducing service delay. The premise of making accurate predictions for business recommendation engines requires a large number of user behavior samples as support. However, the number of samples limited to a single user is insufficient and the types are limited, resulting in low accuracy of business predictions a...

AI Technical Summary

Problems solved by technology

However, there are still some problems. This type of method generally weights and fuses the prediction results of all associated users, which will inevitably introduce sample noise from other users, resulting in low or even lower prediction accuracy, and cannot provide users with good business experience

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