DCAN: Deep Co-Attention Network by Modeling User Preference and News Lifecycle for News Recommendation

Meng, Lingkang; Shi, Chongyang; Hao, Shufeng; Su, Xiangrui

doi:10.1007/978-3-030-73200-4_7

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Ji et al [12] studied the dynamic characteristics of interaction times between users and commodities, utilizing a time-sensitive heterogeneous graph neural network based on commodity recommendation, improving recommendation accuracy and providing better interpretability compared to traditional neural network methods. Meng et al [13] studied the importance of commodity lifecycle, integrating user preference attention and commodity lifecycle, modeling the dual impact of user clicks on commodities. Ji et al [14] used commodity click-through rates to measure commodity popularity.…”

Section: Related Workmentioning

confidence: 99%

“…In terms of selecting comparison methods, we chose several excellent methods in the field of commodity recommendation as benchmarks. These methods include DRN [9], which recommends commodities by simulating feedback after clicks, DCAN [13], which integrates user preference attention and commodity lifecycle, CTR [14], which measures popularity based on click-through rate, and NPA [15], which learns commodity and user representations using attention mechanism networks. By comparing with these methods, we can objectively and accurately evaluate the performance and advantages of our proposed RUICP algorithm in the experiment.…”

Section: A Dataset and Experimental Setupmentioning

confidence: 99%

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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%

Section: A Dataset and Experimental Setupmentioning

confidence: 99%

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

Xu,

Xia

2024

IJACSA

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Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems

Sun

Shang

et al. 2022

Database Systems for Advanced Applications

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A Survey of Personalized News Recommendation

Meng,

Huo,

Zhang

et al. 2023

Data Sci. Eng.

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Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented.

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DCAN: Deep Co-Attention Network by Modeling User Preference and News Lifecycle for News Recommendation

Cited by 5 publications

References 22 publications

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

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

Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems

A Survey of Personalized News Recommendation

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