Large-Scale Real-Time News Recommendation Based on Semantic Data Analysis and Users’ Implicit and Explicit Behaviors

Ficel, Hemza; Haddad, Mohamed Ramzi; Zghal, Hajer Baazaoui

doi:10.1007/978-3-319-98398-1_17

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…For instance, Wang et al [3] introduced a recommendation algorithm that emphasizes user click behavior, inferring preferences by analyzing user browsing patterns to identify points of interest. Similarly, Ficel et al [4] utilized the relationship between users and commodities for recommendations. They first modeled articles based on freshness and popularity then inferred user preferences based on personal information and browsing history, and finally recommended commodities by integrating the two pieces of information.…”

Section: Related Workmentioning

confidence: 99%

RUICP: Commodity Recommendation Model Based on User Real Time Interest and Commodity Popularity

Xu,

Xia

2024

IJACSA

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At present, the recommendation of massive commodities mainly depends on the short-term click through rate of commodities and the data directly browsed and clicked by users. This recommendation method can better meet the shopping needs of users, but there are two shortcomings. One is to recommend homogeneous commodities to long-term shopping users; second, we can't grasp the real-time changes of users' interests, and can only recommend results similar to the recently clicked products. Therefore, this study intends to establish a time-varying expression method of users' interest intensity to solve the deviation of real-time recommendation content, and propose a recommendation model RUICP based on users' timedependent interest and commodity heat. Firstly, the user's basic data and cumulative usage information are used for portrait, specifically, the user's usage data is divided into isochronous and deep-seated semantic feature analysis, the model is optimized and the user's long-term interest intensity is obtained after parameter estimation; Then, the user's short-term interest is obtained by splitting the user's short-term use data, and the user's final interest is calculated by combining the short-term interest and the user's long-term interest intensity; Then calculate the product popularity score by adding the repeated click through rate of products, and then update the ranking of products; Finally, the classic item based collaborative filtering algorithm is used to calculate the matching degree of user interest and goods, and then recommend. The results of simulation experiments show that compared with other methods, RUICP has higher recommendation accuracy for old users and has certain value for solving the cold start problem.

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Section: Related Workmentioning

confidence: 99%