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A multitask recommendation algorithm based on DeepFM and Graph Convolutional Network

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第2期35卷 e7498-e7498页

作者： Chen, Liqiong Bi, Xiaoyu Fan, Guoqing Sun, Huaiying Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai Peoples R China Shanghai Adm Sch Teaching & Res Sect Comp Sci Shanghai Peoples R China Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai 201418 Peoples R China

For a long time, the problems of cold start and sparse data have always been the key problems to be solved by the recommendation system. Researchers usually use auxiliary information to deal with the aforementioned problems, thereby achieving the purpose of enhancing the recommendation effect. For example, the multitask feature learning framework (MKR) uses knowledge graphs as auxiliary information to enhance recommendations. However, the MKR algorithm has the problem of insufficient semantic information representation which affect the recommendation results. Thus, a multitask recommendation algorithm based on DeepFM and graph convolutional network (DeepFM_GCN) is proposed. The graph convolution network is used to deeply mine auxiliary entity information in the knowledge graph to supplement the sparse item semantics information in the recommendation task. Through the method of cross compression unit combined with Deep Neural Network to achieve feature sharing items and entities which to make up for the impact of insufficient feature representation. Then the DeepFM_GCN model utilizes DeepFM to deeply mine the interaction feature of users and items to avoid inaccurate items recommended to users. From the analysis of the experimental results, the DeepFM_GCN model can more fully explore user and item features, accordingly avoiding semantic ambiguity and improving prediction accuracy.

关键词： graph convolutional network knowledge graph multitask learning neural network recommendation algorithm

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A Blended Collaborative Teaching Mode in Language Learning Based on recommendation algorithm

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2021年第23期16卷 111-126页

作者： Wang, Ting Zibo Vocat Inst Zibo Peoples R China

With the continuous expansion of economic globalization, English, as the official language in the world, has a high popularity. It can be related to a better development in the future. Meanwhile, with the continuous progress of technology in modern society, multimedia online teaching has become the main means in colleges and universities, which especially brings great changes to foreign language teaching. However, foreign language teaching is facing many difficulties such as large number of contents, less lessons and low participation of students in class. Therefore, the reform of college foreign language teaching is imperative. Based on blended collaborative teaching mode, the paper has designed a "Three Classes" composed of "Communication Class", "Famous Teacher Class" and "Elite Online Class". Teaching process has been designed according to the cognitive process of "Previewing before class, consolidating in class and expanding after class". In addition, recommendation algorithm has been used to collect data on four dimensions, namely, involvement in foreign language learning, content completion of foreign language learning, interaction in foreign language learning and effectiveness of foreign language learning. A performance evaluation system for foreign language learning supported by big data has been established. The results of teaching practice have proved that the blended collaborative teaching mode implemented in the study can be integrated into the whole teaching process through intelligent data analysis, information sharing and technology application, improving teaching quality, enhancing the interaction between teachers and students, which is worthy of promotion and application.

关键词： blended teaching collaborative learning college foreign language recommendation algorithm

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A collaborative filtering recommendation algorithm based on embedding representation

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Alharbe, Nawaf Rakrouki, Mohamed Ali Aljohani, Abeer Taibah Univ Appl Coll Madinah 42353 Saudi Arabia Univ Tunis Ecole Super Sci Econ & Commerciales Tunis Tunis Tunisia Univ Tunis Business Analyt & DEcis Making Lab BADEM Tunis Business Sch Tunis Tunisia

This aims to propose a collaborative filtering algorithm based on embedding representation and word embedding techniques, namely UI2vec. According to the joint feature extraction network designed in this paper, UI2vec embeds users and items on the potential space at the same time, and uses the item similarity between them to predict the user's content of interest. Then a generative model VUI2vec with more stable performance is proposed based on UI2vec, which maps users and items as independent Gaussian distributions and obtains the approximate posterior distribution of both by variational inference. The recommendation performance of UI2vec and VUI2vec is evaluated on TaFeng, Movielens, and Netflix datasets. The impact of important superparameters within the model on performance is investigated. The experimental results show that compared with the baseline model, the proposed methods performs consistently well.

关键词： recommendation algorithm Deep learning Collaborative filtering Generative model

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AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder

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KNOWLEDGE-BASED SYSTEMS 2023年 259卷

作者： Zhao, Na Long, Zhen Wang, Jian Zhao, Zhi-Dan Yunnan Univ Engn Res Ctr Cyberspace Sch Software Kunming 650504 Yunnan Peoples R China Kunming Univ Sci & Technol Coll Informat Engn & Automat Kunming 650217 Yunnan Peoples R China Shantou Univ Sch Engn Dept Comp Sci Shantou 515063 Guangdong Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou 515063 Guangdong Peoples R China

More and more researches have focused on the use of knowledge graphs (KG) to solve the sparsity problem of traditional collaborative filtering recommendation systems. Most KG based recommen-dation algorithms focus on independent paths connecting users and items, or iteratively propagate user preferences in KG. However, the current approachs that focus on indedpent paths ignore the association between paths. Therefore, in this study, we propose a knowledge graph recommendation system algorithm for the multiple paths RNN encoder (AGRE), which fully considers the association between paths. Specifically, the paths between the user and the item are coded by a specified RNN (MRNN) to accurately learn the user's preferences. Traditional RNNs can encode multiple paths without considering the association between paths, but our RNN can encode multiple paths with considering the association between paths. We have compared AGRE with other state-of-the-art algorithms on three real-world datasets, and achieved good results in terms of AUC and Precision@K. This indicates that AGRE could solve the problem of sparse interaction between users and items, and could make full use of the knowledge graph for recommendation.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Knowledge graph recommendation algorithm Multiple paths MRNN

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The commodity recommendation algorithm for automatic security sale system based on the internet of things

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 4101-4116页

作者： Li, Zhi Yao, Yuxuan Yuan, Yuan Hunan Womens Univ Coll Informat Sci & Engn Zhongyi Yilu 160 Changsha 410004 Peoples R China Hunan Mech & Elect Polytech Coll Informat Engn Changsha Peoples R China

The objective of this paper is to investigate the promptness of recommendation data in automated product recommendation systems, enhancing their ability to facilitate product promotions and compute user similarities. This study also aims to streamline and refine the product purchasing process. To ease the user's decision-making, an innovative design for the user terminal's official account is proposed, creating an effective communication link between users and automated sales products. Second, based on the traditional user-based collaborative filtering recommendation algorithm, user characteristics, time factors, and promotion functions are integrated into the product recommendation algorithm model. To ensure the timeliness of recommendation information and avoid resource waste caused by expired products, time factors and promotion functions are incorporated into the recommendation algorithm model. Finally, a comprehensive test is conducted on the performance of the product recommendation system designed in this experiment. The results show that the average absolute error of the recommended model is reduced by 4.3%, and the improvement effect is significant. The average accuracy rate is increased by about 3.0% compared with the traditional recommendation algorithm. This paper aims to provide important technical support for the improvement of the product recommendation algorithm in the automatic sale system. Besides, this paper provides users with good recommendation services and convenient product purchase processes.

关键词： internet of things recommendation algorithm automatic sale system deep learning user similarity

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Vocational Education Information Technology Based on Cross-Attention Fusion Knowledge Map recommendation algorithm

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2023年第3期22卷

作者： Jiang, Peng Henan Vocat Coll Agr Dept Ideol & Polit Educ Zhengzhou 451450 Peoples R China

With the rapid development of China's economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data.

关键词： Attention mechanism knowledge graph recommendation algorithm vocational education

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Corpus-driven recommendation algorithm for English online autonomous learning resources

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2023年第4-5期33卷 418-432页

作者： Han, Chao Kaifeng Univ Dept Foreign Language Kaifeng 475004 Peoples R China

In order to overcome the problems of the traditional recommendation algorithm of English online learning resources, such as low accuracy, poor convergence and low success rate of recommendation, this paper proposes a recommendation algorithm for online language autonomous learning resources driven by CORUS. Based on the learning vector quantisation (LQN) algorithm, the model of subject word generation based on vector corpus resources is established. In the parameter training of the classification model, the vector weight is normalised to complete the optimisation of the corpus resource classification LVQ subject model. According to the binary particle swarm optimisation algorithm, the personalised recommendation model of autonomous learning resources is implemented. Experimental results show that the proposed vector quantisation network algorithm has high convergence, and the recommended success rate is 99.5%. Therefore, the method proposed in this paper can effectively complete the recommendation of English online autonomous learning resources.

关键词： corpus English learning learning resources recommendation algorithm binary particle swarm

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Dual-Channel Feature Enhanced Collaborative Filtering recommendation algorithm

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FUTURE INTERNET 2023年第6期15卷 215-215页

作者： Ou, Yuanyou Niu, Baoning Taiyuan Univ Technol Coll Informat & Comp Jinzhong 030600 Peoples R China

The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information;the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs;and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels;secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem;then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFECF improved by up to 2.3% and 4.1% on two metrics compared to DCCF.

关键词： recommendation algorithm collaborative filtering transition probability threshold fuzzy c-means clustering time factor

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recommendation algorithm Based on Knowledge Graph to Propagate User Preference

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 1564-1576页

作者： Yang, Zhisheng Cheng, Jinyong Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China

In recommendation algorithms, data sparsity and cold start problems are inevitable. To solve such problems, researchers apply auxiliary information to recommendation algorithms, mine users' historical records to obtain more potential information, and then improve recommendation performance. In this paper, ST_RippleNet, a model that combines knowledge graphs with deep learning, is proposed. This model starts by building the required knowledge graph. Then, the potential interest of users is mined through the knowledge graph, which stimulates the spread of users' preferences on the set of knowledge entities. In preference propagation, we use a triple multi-layer attention mechanism to obtain triple information through the knowledge graph and use the user preference distribution for candidate items formed by users' historical click information to predict the final click probability. Using ST_RippleNet model can better obtain triple information in knowledge graph and mine more useful information. In the ST_RippleNet model, the music data set is added to the movie and book data set;additionally, an improved loss function is used in the model, which is optimized by the RMSProp optimizer. Finally, the tanh function is added to predict the click probability to improve recommendation performance. Compared with current mainstream recommendation methods, ST_RippleNet achieves very good performance in terms of the area under the curve (AUC) and accuracy (ACC) and substantially improves movie, book and music recommendations. (C) 2021 The Authors. Published by Atlantis Press B.V.

关键词： recommendation algorithm Knowledge graph Preference

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recommendation algorithm based on community structure and user trust

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第20期33卷 e6375-e6375页

作者： Lei, Guo Sheng, Yang Shaozi, Li Qingshou, Wu Wensen, Yu Wuyi Univ Sch Math & Comp Sci Wuyishan Peoples R China Fujian Prov Dev & Reform Commiss Digital Fujian Tourism Big Data Inst Wuyishan Peoples R China Fujian Prov Dept Educ Key Lab Cognit Comp & Intelligent Informat Proc Wuyishan Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China

While contemporary community-based recommendation algorithms based on a single community structure are more capable of processing large datasets than ever, they lack recommendation precision. This article proposes a collaborative filtering recommendation algorithm that integrates community structure and user implicit trust. The algorithm first applies a method based on the Gaussian function to fill the matrix of item ratings of users to alleviate data sparsity. It then uses the trust matrix to obtain the asymmetric trust relationship of the trustor and trustee, based on which the degree of users' implicit trust is calculated. The users are divided into communities based on the implicit trust degree to determine the influence among users more accurately. The algorithm then predicts the target user's rating using the ratings of users in the community to generate recommendations. To verify the performance of the proposed algorithm, we compared the proposed algorithm with three contemporary algorithms under the same conditions using FilmTrust datasets. The recommendation accuracy as well as the mean absolute error and root mean square error values of the proposed algorithm were better than those of the other four algorithms by approximately 14% and 4%, respectively. The experimental results demonstrate that the proposed algorithm can achieve better recommendation efficiency than existing algorithms.

关键词： community discovery recommendation algorithm social network trust relationship

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