Abstract: 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' time-dependent 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.

Wenchao Xu and Ling Xia, “RUICP: Commodity Recommendation Model Based on User Real Time Interest and Commodity Popularity” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150322

@article{Xu2024,
title = {RUICP: Commodity Recommendation Model Based on User Real Time Interest and Commodity Popularity},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150322},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150322},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Wenchao Xu and Ling Xia}
}