Tourist commodity recommendation method and system, electronic equipment and storage medium
By constructing user and product feature vectors, clustering and feature supplementation, the problem that tourism product recommendations in online travel services cannot meet user needs is solved, and personalized and intelligent tourism product recommendations are realized, improving user experience and satisfaction.
CN119722213APending Publication Date: 2025-03-28BEIJING CLOUD MANHANG SOFTWARE TECH CO LTD
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Patent Information
- Application Number
- CN202411611714.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-12
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
In the online travel service industry, recommended travel products cannot meet user needs, resulting in poor user experience.
Method used
By obtaining user behavior data and product attribute data, user feature vectors and product feature vectors are constructed, and clustered to obtain user groups and product categories. Feature supplements to new users and new products, and build a travel product recommendation model to generate a personalized recommendation list.
Benefits of technology
Personalized and intelligent travel product recommendations are realized, which can more accurately understand user needs and preferences, improve the accuracy of recommendations, and thus improve user experience and satisfaction.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a tourist commodity recommendation method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining user behavior data and commodity attribute data; constructing a user feature vector and a commodity feature vector according to the user behavior data and the commodity attribute data; performing clustering processing on the user feature vectors and the commodity feature vectors to obtain user groups and commodity categories; according to the user group and the commodity category, performing feature supplementation on the new user and the new commodity to obtain supplementation features; according to the supplementary features, a tourist commodity recommendation model is constructed and trained, and the tourist commodity recommendation model is used for generating a tourist commodity recommendation list according to the current user behavior data and the current user query intention. According to the invention, the technical problem that the recommended tourist commodities cannot meet the requirements of users in the prior art is solved.
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