The design and implementation of personalized news recommendation system

Han, Xiao; Shang, Wenqian; Feng, Shuchao

doi:10.1109/icis.2015.7166653

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Although this method enables rapid recommendations, it may lead to the problem of homogeneous commodities. Han et al [7] personalized commodity recommendations by analyzing user browsing records, employing an improved association rule combined with collaborative filtering algorithms. While this method offers stable overall performance and considers multiple factors for recommendations, it may be constrained when dealing with complex user behavior patterns and may not adequately address the cold start problem for new users.…”

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%

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

Xu,

Xia

2024

IJACSA

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“…The calculation formula for calculating the interest degree of a certain user u in a certain video course j is shown in formula (2). Where N (u) represents the collection of video courses that student user u likes, and S(j, k) represents the collection of k videos similar to video course j.…”

Section: Functional Implementationmentioning

confidence: 99%

“…By integrating artificial intelligence technology into online classroom teaching, a personalized recommendation teaching system can be realized, and then the above problems can be solved. The personalized recommendation teaching system studied in this paper can not only consider the students' personalized demand for video resource course selection, but also recommend the learning content suitable for each student according to their personal preference, thus effectively improving the learning efficiency and enthusiasm of the students [2]. The key way to realize artificial intelligence is machine learning technology.…”

Section: Introductionmentioning

confidence: 99%

Design and Construction of Personalized Recommendation Teaching System Under Artificial Intelligence Background

Liu¹

2023

Atlantis Highlights in Computer Sciences

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Facing the problem of single teaching mode in teaching reform, this paper studies the personalized recommendation teaching system under the artificial intelligence background of sharing teaching resources to improve teaching quality and strengthen students' autonomous learning ability. The system adopts the framework of Springboot + Vue + Mybatis to separate the front end from the back end, and MySQL 8.0.28 is selected to help manage the data. Aiming at the educational demand of information technology, this paper designs and implements a personalized recommendation teaching system under the background of artificial intelligence. This paper introduces the system structure design, the implementation process of collaborative filtering recommendation algorithm CF and other solutions.

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“…A three-layer DNN network model structure is shown in Figure 1. L is the input layer, 1 x , 2 x , 3 x are the input data, 2 L layer is the hidden layer, ( L to the i-th neuron in the k+1 L , add an 0 x , let 0 =1 x , then:…”

Section: Dnnmentioning

confidence: 99%

“…It is one of the main research issues of Natural Language Processing (NLP). Typical applications include judging spam, automatic web page classification [1], sentiment classification [2], and news personalized recommendation [3]. The initial solution is to rely on the word matching method in the document to classify the document [4], but the algorithm is mainly done by manpower, the efficiency is not high, and the classification result can not meet the requirements.…”

Section: Introductionmentioning

confidence: 99%

Grid text classification method based on DNN neural network

et al. 2020

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With the rapid development of network technology, the electric power Internet of Things needs to face a large number of electronic texts and a large number of distributed data access and analysis requirements. If the system wants to complete accurate and efficient data analysis and build an existing data and service standard system covering the entire chain of energy and power business on the existing basis, it must implement massive electronic text retrieval, information extraction and classification in the power grid system. In order to achieve this purpose, a DNN neural network classification model is constructed to classify the text information of the power grid, and the effectiveness of the method is verified by experiments based on data from the substation information system.

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The design and implementation of personalized news recommendation system

Cited by 5 publications

References 3 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

Design and Construction of Personalized Recommendation Teaching System Under Artificial Intelligence Background

Grid text classification method based on DNN neural network

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