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User-centered recommendation using US-ELM based on dynamic graph model in E-commerce

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第4期10卷 693-703页

作者： Ding, Linlin Han, Baishuo Wang, Shu Li, Xiaoguang Song, Baoyan Liaoning Univ Sch Informat Shenyang 110036 Liaoning Peoples R China

The recommender systems can gain the needs and interests of users by analyzing the user history data and then help the users making decisions on appropriate choices in E-commerce. However, with the increasing of data volume and the popularization of information network, the participation of users in E-commerce activities is growing deeply. How to analyze the user preferences and make a user-centered efficient recommendation is an urgent problem to be further researched. In this paper, we first propose the user-centered recommendation based on dynamic graph model to express the user preferences and gain the user preference vectors for recommendation. Then, after gaining the user preferences vectors, we propose the user clustering algorithm using US-ELM to cluster the users into different clusters. Last, we provide two recommendation algorithms, which can present top-k recommendation, respectively the group recommendation and personal recommendation. With the extensive experiments, our recommendation algorithms can effectively express the user preferences and reach a good performance.

关键词： US-ELM Dynamic graph recommendation algorithm User preference

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A 2020 perspective on "DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function"

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2021年 48卷

作者： Zhang, Wen Wang, Qiang Yang, Ye Yoshida, Taketoshi Beijing Univ Technol Coll Econ & Management Res Base Beijing Modern Mfg Dev Beijing Peoples R China Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA Japan Adv Inst Sci & Technol Sch Knowledge Sci 1-1 Ashahidai Nomi City Ishikawa 9231292 Japan

The development of Internet comes up with the prosperity of E-commerce all over the world. In order to promote sales and save consumers' labor in commodity browsing, recommender systems are proposed by E-commerce platforms to provide online consumers with products and services of their potential interests. The primary challenge in recommendation roots in the intricacy in quantifying users' preferences on items with the reality of data sparsity and the computation complexity. Hence, more and more researchers are attempting deep learning techniques to deal with the challenge with the hope of using advanced algorithms to alleviate the intricacy. Word embedding is used to learn the association of items in a space of low dimensionality. Multi-layer perception is used to learn users' preferences on items in a data-driven manner with a customized loss function. The future work of recommender systems includes three folds. The one is to make use of multi-source data to combine implicit and explicit user behavior data to address the problem of data sparsity. The second is dynamic recommendation with the changing users' preferences on items and make recommender systems light-weight and useable in complex scenarios. The third is to provide effective and verifiable recommendation under the premise of user privacy protection

关键词： Recommender systems Deep learning User preference recommendation algorithm Data sparsity Privacy protection

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An Overview of recommendation System Based on Generative Adversarial Networks 2020

An Overview of Recommendation System Based on Generative Adv...

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Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science

作者： Biao Du Lin Tang Lin Liu School of Information Yunnan Normal University Kunming Yunnan China Key Laboratory of Educational Information for Nationalities Ministry of Education Yunnan Normal University Kunming Yunnan China

ISBN: (纸本)9781450389099

With the growth of explosive information resources, information overload has become increasingly serious. Users are eager to find what they need in a large amount of information resources. The recommendation system is an effective solution for filtering information and alleviating information overload. Traditional recommendation algorithms have achieved great results in applications and are widely used, but they also face serious data sparse problems. The proposal of the generative adversarial network provides another idea for modeling implicit feedback information-adversarial learning. By reviewing the research of recommendation algorithms based on generative adversarial networks in recent years, we analyze the differences and advantages of generative adversarial networks and traditional recommendation algorithms, and summarize the practical applications of these algorithms. Finally, the future of recommendation algorithms is discussed based on generative adversarial networks prospects for development trends.

关键词： Data sparseness Generative adversarial network Cold start recommendation algorithm

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A Survey of recommendation algorithms Based on Knowledge Graph Embedding

A Survey of Recommendation Algorithms Based on Knowledge Gra...

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第一届计算机科学与教育信息化国际会议

作者： Chan Liu Lun Li Xiaolu Yao Lin Tang School of Information Yunnan Normal University Key Laboratory of Educational Informatization for Nationalities Ministry of Education Yunnan Normal University

ISBN: (纸本)9781728123097

Recommender system is able to realize personalized information filtering, which is a key way for knowledge discovering in information-rich environment. Knowledge graphs contain rich semantic associations between entities, which can be utilized to strengthen relationships between recommended items and bring interpretability for recommendation. With the establishment of knowledge graphs in various fields, the increasing numbers of researchers have carried out studies on recommendation algorithm based on knowledge graphs. Among them, recommendation algorithm based on knowledge graph embedding is a kind of simple and effective way to introduce knowledge entities and their relation into traditional recommender system. In this paper, the study about this kind of studies is summarized by analysis of existing literatures. We discuss and compare several studies, and the key elements of these algorithms are statistically analyzed.

关键词： recommendation algorithm knowledge graph embedding feature vector

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A New Time-Aware Collaborative Filtering Intelligent recommendation System

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Computers, Materials & Continua 2019年第8期61卷 849-859页

作者： Weijin Jiang Jiahui Chen Yirong Jiang Yuhui Xu Yang Wang Lina Tan Guo Liang Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Hunan University of Technology and BusinessChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China Tonghua Normal University Tonghua134002China School of Bioinformatics University of MinnesotaTwin CitiesUSA

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project ***,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more *** the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large *** experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

关键词： Fuzzy clustering time weight attenuation function Collaborative filtering method recommendation algorithm

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Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 174卷 114771-114771页

作者： Weng, Lijuan Zhang, Qishan Lin, Zhibin Wu, Ling Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China Univ Durham Business Sch Mill Hill Lane Durham DH1 3LB England Fuzhou Univ Sch Math & Comp Sci Fuzhou Peoples R China

Most of the extant studies in social recommender system are based on explicit social relationships, while the potential of implicit relationships in the heterogeneous social networks remains largely unexplored. This study proposes a new approach to designing a recommender system by employing grey relational analysis on the heterogeneous social networks. It starts with the establishment of heterogeneous social networks through the user-item bipartite graph, user social network graph and user-attribute bipartite graph;and then uses grey relational analysis to identify implicit social relationships, which are then incorporated into the matrix factorization model. Five experiments were conducted to test the performance of our approach against four state-ofthe-art baseline methods. The results show that compared with the baseline methods, our approach can effectively alleviate the sparsity problem, because the heterogeneous social network provides richer information. In addition, the grey relational analysis method has the advantage of low requirements for data size and efficiently relieves the cold start problem. Furthermore, our approach saves processing time, thus increases recommendation efficiency. Overall, the proposed approach can effectively improve the accuracy of rating prediction in social recommendations and provide accurate and efficient recommendation service for users.

关键词： Recommender system Heterogeneous social network Grey relational analysis recommendation algorithm Implicit social relationships User profile

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Scalable recommendations using decomposition techniques based on Voronoi diagrams

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INFORMATION PROCESSING & MANAGEMENT 2021年第4期58卷 102566-102566页

作者： Das, Joydeep Majumder, Subhashis Gupta, Prosenjit Datta, Suman Heritage Acad Kolkata WB India Heritage Inst Technol Dept Comp Sci & Engn Kolkata WB India Tata Consultancy Serv Kolkata WB India

Collaborative filtering based recommender systems typically suffer from scalability issues when new users and items join the system at a very rapid rate. We tackle this concerning issue by employing a decomposition based recommendation approach. We partition the users in the recommendation domain with respect to location using a Voronoi Diagram and execute the recommender algorithm individually in each partition (cell). This results in a much reduced recommendation time as we eliminate the need for running the algorithm using the entire user set. We further address the problem of improving the recommendation quality of the users residing in the peripheral region of a Voronoi cell. The primary objective of our approach is to bring down the recommendation time without compromising the accuracies of recommendations much, which is rightly addressed by our proposed method. The outcomes of the experiments performed demonstrate the scalability as well as efficacy of our method by reducing the runtime of the baseline CF algorithm by at least 65% for each of these four publicly available datasets of varying sizes - MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor datasets. The accuracies of recommendations in terms of MAE, RMSE, Precision, Recall and F1 metrics also hold good.

关键词： Voronoi diagrams Collaborative filtering recommendation algorithm Scalability

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Probabilistic Matrix Factorization recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

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IEEE ACCESS 2020年 8卷 208311-208321页

作者： Zhang, Chenkun Wang, Cheng Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information. First, the self-attention mechanism is added to convolutional matrix factorization and a probabilistic matrix factorization model, based on a convolutional neural networks with self-attention mechanism, is proposed. Second, after integrating auxiliary information, such as item comment, item name, and item category, probabilistic matrix factorization, based on a self-attention mechanism convolutional neural networks, is used for recommendation. Adding the self-attention mechanism allows convolutional matrix factorization to capture the long-distance dependence between different components of the auxiliary information. Integrating the item comment, name, and category information alleviates the data sparsity of recommendation, and improves the accuracy of rating prediction. Experimental results on the MovieLens-1M and MovieLens-10M datasets show that the probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item comment, name, and category information is superior to existing popular methods, in respect of root mean square error.

关键词： Probabilistic logic Convolutional neural networks Root mean square recommendation algorithm self-attention mechanism convolutional neural networks probabilistic matrix factorization item auxiliary information

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Personalized Knowledge recommendation Based on Knowledge Graph in Petroleum Exploration and Development

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第10期34卷

作者： Huang, Gang Yuan, Man Li, Chun-Sheng Wei, Yong-he Northeast Petr Univ Coll Comp & Informat Technol Daqing Peoples R China JiBei Elect Power Ltd Co State Grid Corp China Management Training Ctr Beijing Peoples R China

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where K value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users' potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

关键词： recommendation algorithm knowledge graph petroleum exploration and development

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Toward developing a metastatic breast cancer treatment strategy that incorporates history of response to previous treatments

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BMC CANCER 2021年第1期21卷 212-212页

作者： Olow, Aleksandra K. van't Veer, Laura Wolf, Denise M. Univ Calif San Francisco Dept Lab Med Helen Diller Family Comprehens Canc Ctr San Francisco CA 94115 USA Merck Res Labs 213 E Grand Ave San Francisco CA 94080 USA

BackgroundInformation regarding response to past treatments may provide clues concerning the classes of drugs most or least likely to work for a particular metastatic or neoadjuvant early stage breast cancer patient. However, currently there is no systematized knowledge base that would support clinical treatment decision-making that takes response history into *** model history-dependent response data we leveraged a published in vitro breast cancer viability dataset (84 cell lines, 90 therapeutic compounds) to calculate the odds ratios (log (OR)) of responding to each drug given knowledge of (intrinsic/prior) response to all other agents. This OR matrix assumes (1) response is based on intrinsic rather than acquired characteristics, and (2) intrinsic sensitivity remains unchanged at the time of the next decision point. Fisher's exact test is used to identify predictive pairs and groups of agents (BH p<0.05). recommendation systems are used to make further drug recommendations based on past 'history' of *** the 90 compounds, 57 have sensitivity profiles significantly associated with those of at least one other agent, mostly targeted drugs. Nearly all associations are positive, with (intrinsic/prior) sensitivity to one agent predicting sensitivity to others in the same or a related class (OR>1). In vitro conditional response patterns clustered compounds into five predictive classes: (1) DNA damaging agents, (2) Aurora A kinase and cell cycle checkpoint inhibitors;(3) microtubule poisons;(4) HER2/EGFR inhibitors;and (5) PIK3C catalytic subunit inhibitors. The apriori algorithm implementation made further predictions including a directional association between resistance to HER2 inhibition and sensitivity to proteasome *** drug sensitivity conditioned on observed sensitivity or resistance to prior drugs may be pivotal in informing clinicians deciding on the next line of breast cancer treatments for patients wh

关键词： Metastatic breast cancer Resistance recommendation algorithm

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