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作者： Junxiang Zhao Southwest University

Among various recommendation algorithms,collaborative filtering stands out as a popular and effective technique that analyzes user behavior to suggest personalized *** goal of studying recommendation algorithms is to enhance the exactness of recommendation outcomes and provide users with tailored *** this paper,the algorithm of collaborative filtering is divided into User-CF and Item-CF,and their algorithm principles and implementation steps are *** similarity calculation of both methods is improved,and a comparison of their advantages and disadvantages is *** are conducted on the MovieLens-latest-small movie dataset to compare the performance of User-CF and Item-CF before and after their similarity calculation is *** results show that the similarity improvement of User-CF can substantially enhance the algorithm's inclusiveness,while the similarity improvement of Item-CF does not have a considerable impact on the algorithm's performance ***,the comparison shows that Item-CF has better coverage and popularity,while User-CF has better precision and recall.

关键词： collaborative filtering recommendation algorithm User-CF Item-CF similarity calculation movie recommendation

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An intelligent E-commerce recommendation algorithm based on collaborative filtering technology 7

An intelligent E-commerce recommendation algorithm based on ...

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7th International Conference Intelligent Computation Technology Automation (ICICTA)

作者： Qing, Yang Xiao Henan Tech Coll Construct Zhengzhou 450007 Henan Province Peoples R China

ISBN: (纸本)9781479966363

This paper presents an intelligent E-commerce recommendation algorithm with collaborative filtering algorithm. Firstly, a novel user interest model is given, which is an important module in the E-commerce recommendation system. Particularly, to effectively integrate the user interest model with collaborative filtering algorithm, we assume that if two users have similar interest vector, they may want to choose the same products. Furthermore, we define the users with similar interests as neighbor, and finding the neighbors is of great importance in the E-commerce recommendation. Secondly, the intelligent E-commerce recommendation algorithm is proposed based on user-rating matrix, and the products with highest scores is recommendated to the target user. Finally, experiments are conducted to make performance. Compared with other two schemes using four metrics, it can be seen that the proposed algorithm is more suitable to be used in E-commerce recommendation system.

关键词： recommendation algorithm Collaborative filtering Weight matrix User similarity Neighbor

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Graph Convolution Network and User Interest Modeling for Enhanced recommendation Systems 24

Graph Convolution Network and User Interest Modeling for Enh...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Xu, Zihang Chu, Chiawei City Univ Macau Fac Data Sci Macau Peoples R China Zhuhai Coll Sci & Technol Aliyun Coll Data Applicat Zhuhai Peoples R China

ISBN: (纸本)9798400716959

In the realm of modern recommendation systems, efficiently handling the vast amount of available data and accurately predicting user preferences remains a significant challenge. Traditional models often struggle with sparse data and fail to capture the dynamic nature of user interests. This paper introduces an innovative solution, the Hybrid User Interest Knowledge Graph and Graph Convolutional Network (HUIKGKCN), which is specifically designed to address these challenges. HUIKGKCN revolutionizes recommendation systems by focusing on the intricacies of user behavior and the complex web of user-item relationships. The model employs Long Short-Term Memory (LSTM) networks and advanced attention mechanisms to delve into the user's historical interactions, extracting refined interest features that reflect both the temporal dynamics and the varying importance of each action. This nuanced approach ensures a more accurate and personalized user profile. Moreover, the model overcomes the limitations of traditional embedding methods like TransR by incorporating the GC-OTE embedding method. This novel approach adeptly handles various complex relational scenarios, including many-to-one, one-to-many, and many-to-many relationships, thus significantly enhancing the model's ability to understand and predict user preferences. The unique strength of HUIKGKCN lies in its use of heterogeneous propagation. By integrating knowledge graphs with user-item interaction information, the model not only reduces knowledge noise but also encodes collaborative signals more effectively. The use of a knowledge-aware attention function further refines the process, focusing on essential information and minimizing irrelevant data. The efficacy of HUIKGKCN has been rigorously tested and validated on two diverse datasets, Book_Crossing and ***. The results are impressive, showing a marked improvement over several baseline models across various critical metrics. These outcomes highlight the model's ro

关键词： recommendation algorithm collaborative knowledge graph attention mechanism graph convolution network

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Enhancing User Experience in Ultra HD Cloud Performing Arts Live Streaming: A QoS-to-QoE Mapping Approach

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IEEE TRANSACTIONS ON BROADCASTING 2024年第2期70卷 413-428页

作者： Yang, Li Liu, Jianzhang Li, Shufeng Zhang, Deyou Xia, Zhiping Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China KTH Royal Inst Technol Div Informat Sci & Engn S-10044 Stockholm Sweden China Acad Broadcasting Sci Natl Radio & Televis Adm Beijing 100024 Peoples R China

Video streaming services have gradually become the dominant use case on the Internet, where people's focus has shifted from a service-centric approach to a user-centric approach. This paper proposes a continuous Quality of Experience (QoE) evaluation method based on Quality of Service (QoS) parameters, which combines the advantages of subjective and objective research methods. The proposed method can accurately calculate the achievable QoE based on user QoS. Additionally, we develop a QoE-Ensemble MLP prediction model, employing ensemble learning and MLP techniques, to overcome limitations of the QoE evaluation method and accurately predict user QoE based on QoS parameters. Furthermore, we propose a low-complexity network bandwidth allocation algorithm based on a QoE prediction model to help service providers minimize network bandwidth waste while meeting user QoE requirements. Finally, the experiments show that our QoE evaluation model and network bandwidth allocation algorithm have better performance. And according to the result analysis, we also got the connection between QoS parameters and QoE.

关键词： Quality of experience Quality of service Streaming media Predictive models User experience Bandwidth Data models UHD cloud performing arts live streaming QoS-to-QoE mapping ensemble learning recommendation algorithm bandwidth resource optimization

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Eir-Ripp: Enriching Item Representation for recommendation with Knowledge Graph 6th

Eir-Ripp: Enriching Item Representation for Recommendation w...

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM)

作者： Li, Kaiwen Ye, Chunyang Wang, Jinghui Hainan Univ Haikou 570203 Hainan Peoples R China

ISBN: (纸本)9783031252006;9783031252013

To improve the performance of recommendation models and enhance the interpretability of results, knowledge graph is often used to add rich semantic information to the recommended items. Existing methods either use knowledge graph as an auxiliary information to mine users' interests, or use knowledge graph to establish relationships between items via their hidden information. However, these methods usually ignore the interaction between users and items. As a result, the hidden relationship between users and items are not well explored in the item representation. To address this issue, we propose an enhancement model to learn item representation based on RippleNet (Eir-Ripp). By mining the users' historical behavior and user characteristics, users' preference and the correlation between users and items are extracted to complement the semantic information of items. We conduct extensive experiments to evaluate our proposal on three public data sets. Experimental results show that our model outperforms the baseline methods in terms of an up to 8.8% improvement in the recommendation.

关键词： Knowledge graph User interest Item representation recommendation algorithm

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How Misinformation Diffuses on Online Social Networks: Radical Opinions, Adaptive Relationship, and algorithmic Intervention

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年

作者： Zhang, Mengyi Dong, Qingxing Wu, Xiaozhen Wuhan Univ Natl Inst Cultural Dev Wuhan 430072 Peoples R China Wuhan Univ Sch Journalism & Commun Wuhan 430072 Peoples R China

The advent of information distribution mechanism constituted by self-exploration, network neighbors, and especially algorithms, has aroused widespread concerns about the reinforcement of misinformation beliefs and the resulting polarization. However, few existing researches fully consider the inherent characteristics of misinformation (e.g. evoking repulsive effects), as well as the adaptive nature of social relationship or come to see the impacts of algorithmic interventions on online misinformation and the formation process of social groups. To comprehensively investigate the coevolution process of user misinformation beliefs and social relationships under algorithmic interventions, we proposed a novel model with configurations as follows: 1) a nonlinear social influence function constructed to reflect the process of reinforcing misinformation beliefs;2) probabilities for the rewiring of links among individuals determined by their opinion distance and social distance;and 3) multiple algorithmic mechanisms reformulated, regarding five recommendation processes and the information distribution rules integrating three information sources. Such extensive numerical simulation experiments have revealed diversification, radicalization, and polarization of misinformation. We observe that the introduction of moderate repulsive interactions fosters the emergence of diverse opinions. In absence of algorithmic interventions, misinformation naturally evolves into radicalization, while the introduction of algorithmic interventions exacerbates polarization, particularly with extensive reliance on content-based recommendations and excessive allowance of distributed opinions from recommendations. It is noteworthy that we discover that encouraging recommendation based on predetermined information effectively reverses the trend of misinformation evolution. Our research contributes to clarifying the interaction between human behavior and artificial intelligence, as well as providing i

关键词： Heuristic algorithms Fake news Social networking (online) Recommender systems Market research Cultural differences Social factors Psychology Logic gates Human factors Complex network misinformation diffusion opinion dynamics polarization recommendation algorithm

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Teaching Design Model of Media Courses Based on Artificial Intelligence

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IEEE ACCESS 2024年 12卷 121242-121250页

作者： Liu, Xing Jinan Univ Pearl River Film Acad Sch Art Guangzhou 510632 Peoples R China

Under the background of information technology, in order to promote the smooth development of teaching design of media courses, we must adhere to the basic principle of taking students as the center and reasonably create teaching situations. Insert high-flying wings for the production and dissemination of intelligent media content in the AI era, making content production faster and more efficient, realizing scene and experience of content terminals, and making information dissemination more intelligent and personalized. This topic launches the teaching design of media courses based on AI (artificial intelligence). The teaching scheduling model of media courses and the teaching recommendation algorithm of media courses based on AI are established. By introducing LSTM (Long Short-Term Memory) to embed the multi-feature information of users and courses into the model, users' interests and preferences can be fully understood, and satisfactory recommendation results can be given. The results show that the accuracy of CF(collaborative filtering) recommendation based on clustering is higher than that of this recommendation algorithm. The accuracy and recall of this recommendation algorithm can reach 91.4619% and 87.7128% respectively. On the one hand, the cold start problem of CF algorithm is improved, on the other hand, compared with the content-based recommendation algorithm, the effect is significantly improved, and the expected effect is achieved.

关键词： Educational courses Media Artificial intelligence Long short term memory Job shop scheduling Clustering algorithms Recommender systems algorithm design and analysis media courses teaching design recommendation algorithm

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A collaborative filtering recommendation algorithm combined with user and item

A collaborative filtering recommendation algorithm combined ...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Liu, Fengming Li, Haixia Dong, Peng Shandong Normal Univ Sch Management Sci & Engn Jinan Peoples R China

ISBN: (纸本)9783038350125

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didn't evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

关键词： Collaborative filtering recommendation algorithm User Item Sparseness

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RTN-GNNR: Fusing Review Text Features and Node Features for Graph Neural Network recommendation

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IEEE ACCESS 2022年 10卷 114165-114177页

作者： Xiao, Bohuai Xie, Xiaolan Yang, Chengyong Wang, Yuhan Guilin Univ Technol Sch Informat Sci & Engn Guilin 541004 Peoples R China Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin 541004 Peoples R China Guilin Univ Technol Network & Informat Ctr Guilin 541004 Peoples R China

Recent recommendation systems have achieved good results by applying Graph Neural Network (GNN) to user-item interaction graphs. However, these recommendation systems can only handle structured interaction data and cannot handle unstructured review text data well. Based on the user-item interaction graph, combining review text can effectively solve the problem of data sparsity and improve recommendation quality. Most of the current recommendation methods combining review texts stitch the data from different modalities, leading to insufficient interactions and degrading the recommendations' performance. A model called RTN-GNNR to fuse Review Text feature and Node feature for Graph Neural Network recommendation is proposed to solve these problems and get better item recommendations. RTN-GNNR consists of four modules. The review text feature extraction module proposes a Bi-directional Gated Recurrent Unit (Bi-GRU) text analysis method that combines Bidirectional Encoder Representation from Transformers (BERT) and attention mechanism to enable the model to focus on more valuable reviews. The node feature extraction module proposes a GNN combined with the attention mechanism for the interactive node extraction method, which enables the model to have better higher-order feature extraction capability. The feature fusion module proposes the method of tandem Factorization Machine (FM) and Multilayer Perceptron (MLP) to realize interactive learning among multi-source features. The prediction module inner-products the fused higher-order features to achieve recommendation effect. We conducted experiments on five publicly available datasets from Amazon, showing that RTN-GNNR outperforms state-of-the-art personalized recommendation methods in both RMSE and MSE, especially in the sparser two datasets. The effectiveness of each module of the model is also demonstrated by a comparison of the ablation experiments.

关键词： Feature extraction Classification algorithms Data models Collaborative filtering Recommender systems Data integration Graph neural networks recommendation algorithm graph neural network review-based recommendation multimodal data fusion attentional mechanism data sparsity

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A Self-Attention Mask Learning-Based recommendation System

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IEEE ACCESS 2022年 10卷 93017-93028页

作者： Aljohani, Abeer Rakrouki, Mohamed Ali Alharbe, Nawaf Alluhaibi, Reyadh Taibah Univ Appl Coll Madinah 1089 Saudi Arabia Univ Tunis Ecole Super Sci Econ & Commerciales Tunis Tunis 1938 Tunisia Univ Tunis Tunis Business Sch Business Analyt & Decis Making Lab BADEM Tunis 1938 Tunisia Taibah Univ Coll Comp Sci & Engn Dept Comp Sci Madinah 1089 Saudi Arabia

The primary purpose of sequence modeling is to record long-term interdependence across interaction sequences, and since the number of items purchased by users gradually increases over time, this brings challenges to sequence modeling to a certain extent. Relationships between terms are often overlooked, and it is crucial to build sequential models that effectively capture long-term dependencies. Existing methods focus on extracting global sequential information, while ignoring deep representations from subsequences. We argue that limited item transfer is fundamental to sequence modeling, and that partial substructures of sequences can help models learn more efficient long-term dependencies compared to entire sequences. This paper proposes a sequence recommendation model named GAT4Rec (Gated Recurrent Unit And Transformer For recommendation), which uses a Transformer layer that shares parameters across layers to model the user's historical interaction sequence. The representation learned by the gated recurrent unit is used as a gating signal to filter out better substructures of the user sequence. The experimental results demonstrate that our proposed GAT4Rec model is superior to other models and has a higher recommendation effectiveness.

关键词： Transformers Biological system modeling Behavioral sciences Logic gates Recommender systems Encoding Machine learning recommendation algorithm machine learning sequence recommendation model

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