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International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Gegentana Hulunbuir Univ Hulunbuir 021000 Inner Mongolia Peoples R China

ISBN: (纸本)9781728144634

A recommendation algorithm is put forward for the potential candidate recommendation technology in human resources. The dynamic decision diagram is combined in this method based on collaborative filtering technology and partition clustering technology. Firstly, the computational matrix that integrates the dynamic decision diagram recommendation algorithm is established so that the algorithm can recommend information in reference to the matrix. Secondly, the assignment range of the matrix is improved, so that the recommendation algorithm can integrate the evaluations of all users. Finally, the evaluation value and the update coefficient are added, which makes the dynamic update of the algorithm possible, thus recommending the most satisfactory information to the users. On this basis, an advanced recommendation algorithm based on partition clustering is proposed, which has further improved the precision and real-time performance of the algorithm. In addition, experiments are carried out to verify that the ultimate commendation algorithm based on partition clustering is the optimal recommendation algorithm for potential candidates in human resources, which can provide the most satisfactory information to users.

关键词： Potential Candidates in Human Resources recommendation algorithm Dynamic Decision Diagram

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recommendation algorithm for Mobile E-commerce Based on Reputation 19

Recommendation Algorithm for Mobile E-commerce Based on Repu...

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11th International Conference on Machine Learning and Computing (ICMLC)

作者： Chai, Yuan Li, Dong Wu, Yuchen Qinghai Univ Xining Qinghai Peoples R China Southwest Petr Univ Chengdu Sichuan Peoples R China

ISBN: (纸本)9781450366007

In the new technical background, more and more goods appear in front of the user. Unfortunately, users are increasingly easy to get lost in the massive commodity information. In order to improve the users experience of mobile e-commerce, combined with the advanced technology of large data age, there have been many recommendation algorithms for mobile e-commerce. All of this recommendation algorithms based on customer interests, sales and other different considerations, in order to achieve a purposeful and efficient screening recommendation information. In many ways of thinking, the user reputation as a new era of the spirit of the contract reflects the important value. In this paper, we focus on the current technical situation, designing a set of scoring algorithm through the analysis of the previous behavioral data modeling. After this algorithm, from different customer categories of different needs to start using the mainstream for the score of the business recommendation algorithm algorithm to achieve. And the results are compared with the expected analysis in order to obtain the current algorithm in the credibility of the algorithm under the improvement and adjustment. Finally, we will design a set of recommendation algorithm based on the reputation standards.

关键词： recommendation algorithm collaborative filtering reputation

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recommendation algorithm Based on Multi-Label Clustering and Core Users 22

Recommendation Algorithm Based on Multi-Label Clustering and...

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22nd IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Peiyu Wang, Peipei Wang, Ru Liu, Wanfeng Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781538614822

With the rapid growth of user scale, it is so meaningful to explore the users who carry more valuable information in the user group. The importance of the individual users in the recommender system can improve the recommendation efficiency of the recommender system, enhance the robustness of the recommender system,but there is little research work in this path and can not determine a more effective *** solve this problem, we propose a method based on multi-label clustering to determine core user,and define the concept of correlation between user and label cluster , user location weight,considered the potential relationship between users and *** the same time, we proposed a new method based on multi-label clustering and core user,according to experiments demonstrate the effectiveness of the proposed algorithm, we have a more significant upgrade in the recommendation accuracy and diversity.

关键词： Multi-label Clustering Density Peak Clustering Core User recommendation algorithm

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recommendation algorithm Based on Refined Knowledge Graphs and Contrastive Learning 17th

Recommendation Algorithm Based on Refined Knowledge Graphs a...

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17th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Wang, Bing Yang, Xiaoling Zhang, Xingpeng Zhao, Chunlan Wang, Chunhao Feng, Weishan Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu Peoples R China Southwest Petr Univ Sch Sci Chengdu Peoples R China

ISBN: (纸本)9789819755004;9789819755011

Knowledge graphs are a vital tool for improving recommendation performance and interpretability since they are rich in auxiliary data. Two obstacles must be overcome in the recommendation process, though: 1) Real-world knowledge graphs are frequently noisy and contain connections unrelated to items and entities;2) Data sparsity is a problem due to the long-tail distribution of user-item interactions. To address the aforementioned issues, a recommendation algorithm based on refined knowledge graphs and contrastive learning (RKGCL) is proposed. Firstly, the algorithm utilizes a graph pruning strategy to trim task-irrelevant knowledge associations, obtaining a high-quality knowledge graph. Secondly, it employs a graph convolutional network based on attention mechanisms to learn item embeddings. Subsequently, a simple yet effective noise-based embedding enhancement is applied for crosslayer contrastive learning, thereby alleviating the data sparsity issue. Finally, a message propagation strategy is employed to obtain user and item embeddings for recommendation prediction. Experimental results on three public datasets - Yelp, amazon-book, and Last-FM - show that our model outperforms other benchmark models on Recall@K and NDCG@K.

关键词： recommendation algorithm Knowledge Graph Contrastive Learning Graph Convolutional Networks

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recommendation algorithm Based on Federated Multi-modal Learning 49

Recommendation Algorithm Based on Federated Multi-modal Lear...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Feng, Chenyuan Feng, Zhenyu Wang, Qing Shenzhen Univ Guangdong Hong Kong Joint Lab Big Data Imaging & Shenzhen Peoples R China ZTE Corp Natl Key Lab Mobile Network & Mobile Multimedia T Shenzhen Peoples R China

ISBN: (纸本)9798350374520;9798350374513

recommendation algorithm is a crucial tool for personalized information filtering. However, traditional recommendation algorithms typically need the collection of raw user data in a centralized server for analysis. Deploying the recommendation algorithm in a federated learning (FL) manner is a promising solution, which can train a good-quality model on the premise that the server does not directly access the sensitive user data, in light of the growing attention to privacy security. Nevertheless, the majority of recommendation algorithm researches has only considered uni-modality learning. To fill the gap, this paper designs a federated multi-modal RS. Firstly, an adaptive multi-modal federated recommendation algorithm is proposed to extract features from the item image, the commodity introductory text, and user historical ratings. The algorithm also address the issue of missing modalities via a modal completion mechanism. Additionally, an attention mechanism is designed to distinguish the importance of various modal data on model training, wherein different weight ratios are assigned to various features to realize an adaptive multi-modal RS. Furthermore, one multi-modal dataset is established to verify the efficacy of the proposed algorithm. The simulation results show that our proposed measures contribute a reliable and high-quality recommendation model.

关键词： Federated Learning multi-modal learning recommendation algorithm attention mechanism

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recommendation algorithm for agricultural and sideline products on e-commerce platforms based on deep learning

Recommendation algorithm for agricultural and sideline produ...

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Chen, Lisi School of Computer Science and Engineering Guangzhou Institute of Science and Technology China

ISBN: (纸本)9798331520816

Aiming at the problem of low accuracy in e-commerce platform recommendation of agricultural and sideline products, an agricultural and sideline product recommendation algorithm based on deep learning was studied. Fully analyze the problems existing in the process of recommending agricultural and sideline products, study the mechanism of item-based collaborative filtering algorithm, and combine the document topic and matrix decomposition hybrid algorithm. Highlighting the advantages of both, a topic weighted fusion collaborative filtering algorithm was formed. Through experiments, this algorithm can effectively improve the recommendation effect of agricultural and sideline products on e-commerce platforms. Compared with the simple item collaborative filtering algorithm, the algorithm studied in this study can effectively improve the accuracy of recommended targets by about 5%, and also has a 2% improvement in the diversity of recommended targets. In terms of accuracy of prediction results, through experimental comparison, the root mean square error of this algorithm is also lower than other comparative algorithms, which proves the advantages of this algorithm. © 2024 IEEE.

关键词： Agricultural and sideline products E-commerce platform New media recommendation algorithm

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recommendation algorithm Based On Tag Time Weighting 3

Recommendation Algorithm Based On Tag Time Weighting

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3rd International Conference on Smart City and Systems Engineering (ICSCSE)

作者： Liu Feng Guo Wei-wei Heilongjiang Univ Technol Jixi 158100 Peoples R China

ISBN: (纸本)9781728113661

There are two problems in recommendation System: data sparseness and cold start. To deal with the problems of cold start and recommendation accuracy caused by the "information overload" of the computer network, the author came up with a recommendation algorithm which is based on tag time weighting The algorithm can effectively alleviate the cold start problem. First, the content and implementation process of the algorithm were introduced. Then, using the data sets, the proposed time-weighted label network structure algorithm, also called as TT-WN, was compared with the traditional collaborative filtering algorithm) and the classical network structure algorithm. Finally, the experimental results were compared and analyzed. The results showed that the proposed new time-weighted label network structure algorithm (TT-WN) did a good job in the experimental data sets, and the hit rate and sorting accuracy were much better than the classical network structure algorithm and the traditional collaborative filtering algorithm algorithms. Naturally, the conclusion that the TT-WN algorithm has many advantages was achieved.

关键词： recommendation System Time Weight Label System recommendation algorithm

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recommendation algorithm for agricultural products based on attention factor decomposer and knowledge graph

Recommendation algorithm for agricultural products based on ...

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Asia Conference on algorithms, Computing and Machine Learning (CACML))

作者： Xie, Honghui Yang, Jun Huang, Cong Wang, Zhen Liu, Yi Jiangxi Agr Univ Software Coll Nanchang Jiangxi Peoples R China Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (数字)9781665482905

ISBN: (纸本)9781665482905

To alleviate the distress of data sparsity and cold start in agricultural products e-commerce platforms, this paper proposes an agricultural products recommendation algorithm based on the combination of attention factor decomposer and knowledge graph. The algorithm constructs a knowledge graph for the produce dataset, models the higher-order connectivity of the produce knowledge graph in an end-to-end manner under the space of the knowledge graph, recursively propagates embeddings from the neighbors of the nodes, and extracts the potential feature vectors of the produce by using the attention factor decomposer as the message aggregation of the neighboring nodes. Using MLP, the agricultural product feature vectors and user embedding vectors are integrated and sent to the prediction module, and user click-through rate prediction is obtained by vector inner product operation. Experimenting on an agricultural e-commerce dataset, the ACC and AUC are improved by 1.60% and 1.14%, respectively, compared with the optimal baseline model KGCN. Thus, it verifies the effectiveness as well as feasibility of the improved algorithm on agricultural products data, which can provide a new idea and method for agricultural products e-commerce platform.

关键词： component Agricultural products attention factor decomposer knowledge graph recommendation algorithm

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The application of the matrix decomposition recommendation algorithm based on attribute coupling is studied in the internet of things

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 6081-6089页

作者： Li, Xiaohong Li, Maolin Shaoyang Univ Prov Key Lab Informat Serv Rural Area Southwester Shaoyang Hunan Peoples R China Shaoyang Univ Coll Informat Engn Shaoyang Hunan Peoples R China

Traditional information recommendation system using only the user's score is calculated and recommended, although to a certain extent, can obtain the implied characteristics of users or resources, but the lack of enough semantic interpretation, affecting the effects of recommendation. This article studied and analyzed the recommendation based on attribute coupled matrix decomposition algorithm in the application of Internet of things, on the foundation of the matrix decomposition model successively introduced global offset and time offset, in order to improve the prediction accuracy and the quality is recommended. In this paper, the algorithm is proved by experiment and the prediction accuracy of the algorithm is improved.

关键词： Property coupling matrix decomposition recommendation algorithm internet of things

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A deep neural networks based recommendation algorithm using user and item basic data

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第4期11卷 763-777页

作者： Bi, Jian-Wu Liu, Yang Fan, Zhi-Ping Northeastern Univ Dept Informat Management & Decis Sci Sch Business Adm Shenyang 110169 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

User basic data (e.g. user gender, user age and user ID, etc.) and item basic data (e.g. item name, item category, etc.) are important side data that can be used to enhance the performance of recommendation algorithms, whereas attempts concerning this issue are still relatively scarce. In this study, a deep neural networks based recommendation algorithm is proposed where user average rating, user basic data (user gender, user age, user occupation, user ID), item basic data (item name, item category, item ID) and item average rating are used. The main idea of the algorithm is to build a regression model for predicting user ratings based on deep neural networks. For this, according to the user data (user average rating and user basic data) and the item data (items basic data and item average rating), a user feature matrix and an item feature matrix are respectively constructed using the four types of neural network layers [i.e., embedding layer (EL), convolution layer (CL), pooling layer (PL) and fully connected layer (FCL)]. Then, based on the obtained user feature matrix and item feature matrix, a user-item feature matrix is further constructed using a FCL. On this basis, a regression model for predicting user ratings can be trained, and a recommendation list can be generated according to the predicted user ratings. To verify the effectiveness of the proposed algorithm, three experiments are conducted using the real data from the MovieLens website. The results of experiments show that the proposed algorithm not only outperforms the state-of-the-art collaborative filtering (CF) recommendation algorithms but also alleviates the data sparsity problem and cold-start problem that would occur when the state-of-the-art CF recommendation algorithms are used.

关键词： recommendation algorithm Deep neural networks Side data Collaborative filtering Sparsity problem Cold-start problem

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