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Collaborative filtering recommendation algorithm integrating time windows and rating predictions

APPLIED INTELLIGENCE 2019年第8期49卷 3146-3157页

作者： Zhang, Pengfei Zhang, Zhijun Tian, Tian Wang, Yigui Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250001 Shandong Peoples R China

This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization processing, these two nonnegative matrixes provide useful probability semantics. The posterior distribution of the real part of the probability model is calculated by the variational inference method. Finally, the preferences for items that users have not rated can be predicted. The user-item rating matrix is supplemented by a preference prediction value, resulting in a dense rating matrix. Finally, time weighting is integrated into the rating matrix to construct the 3D user-item-time model, which gives the recommendation results. According to experiments using open Netflix, MovieLens, and Epinion datasets, the proposed algorithm is superior to several existing recommendation algorithms in terms of rating predictions and recommendation effects.

关键词： probability matrix factorization Time weighting probability model Recommendation algorithm

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Improved collaborative filtering for cross-store demand forecasting

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Liang, Mengkun Yang, Liping Li, Kai Zhai, Hao Beijing Univ Chem Technol Sch Econ & Management Beijing Peoples R China Univ Sci & Technol China Sch Management Hefei Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China

Accurate and reliable demand forecasting is crucial for decision -making in assortment planning, store location, and various operational aspects. This study proposes a hybrid TS-SimPMF framework for multi -store multiproduct demand forecasting, employing probabilistic matrix factorization (PMF) with time -series similarity measures to capture product and store heterogeneity. The heterogeneity enables the framework to achieve multi -step demand forecasting under unknown item -store combinations through an adaptive integration strategy. We first employ a hybrid dynamic regularization approach to measure the similarity of product sales simultaneously, and then the PMF framework is applied to generate latent vectors of products and stores. The performance of the proposed framework is verified by the real -world retail data, in which the dataset includes all sales records with 50 SKUs in 10 locations, totaling over 180,000 sales records. These experimental results demonstrate the superior performance of the proposed hybrid model compared to state-of-the-art methods.

关键词： Demand forecasting probability matrix factorization Collaborative filtering Time series Machine learning

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A Novel Collaborative Filtering Algorithm and its Application for Recommendations in E-Commerce

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Computer Modeling in Engineering & Sciences 2021年第3期126卷 1275-1291页

作者： Jie Zhang Juan Yang Li Wang Yizhang Jiang Pengjiang Qian Yuan Liu School of Design Jiangnan UniversityWuxi214122China Department of Clothing Design and Engineering Nantong UniversityNantong226001China Research Center for Intelligence Information Technology Nantong UniversityNantong226001China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China Management and Science University Shah AlamSelangor40100Malaysia

With the rapid development of the Internet,the amount of data recorded on the Internet has increased *** is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of *** this study,we propose a novel collaborative filtering algorithm for generating recommendations in *** study has two main ***,we propose a mechanismthat embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or ***,we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix *** compared the proposed method with several state-of-the-art alternatives on real *** experimental results show that our proposed method outperforms the prevailing approaches.

关键词： Collaborative filtering temporal behavior probability matrix factorization

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Health Risk Prediction Using Big Medical Data - a Collaborative Filtering-Enhanced Deep Learning Approach 20

Health Risk Prediction Using Big Medical Data - a Collaborat...

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20th IEEE International Conference on e-Health Networking, Applications and Services (HEALTHCOM)

作者： Li, Xin Li, Juan North Dakota State Univ Comp Sci Dept Fargo ND 58105 USA

ISBN: (纸本)9781538642948

The massive amount of medical data accumulated from patients and healthcare providers has become a vast reservoir of knowledge source that may enable promising applications such as risk predictive modeling, clinical decision support, disease or safety surveillance. However, discovering knowledge from the big medical data can be very complex because of the nature of this type of data: they normally contain large amount of unstructured data;they may have lots of missing values;they can be highly complex and heterogeneous. To address these challenges, in this paper we propose a Collaborative Filtering-Enhanced Deep Learning approach. In particular, we estimate missing values based on patients' similarity, i.e., we predict one patient's missing features based on the values of similar patients. This is implemented with the Collaborative Topic Regression method, which tightly couples topic model and probability matrix factorization and is able to utilize the rich information hidden in the data. Then a deep neural network-based method is applied for the prediction of health risks. This method can help us handle complex and multi-modality data. Extensive experiments on a real-world dataset have been performed and the results show improvements of our proposed algorithm over the state-of-the-art methods.

关键词： deep neural network medical data topic model probability matrix factorization health risk prediction diabetes

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User Familiar Degree-Aware Recommender System

User Familiar Degree-Aware Recommender System

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IEEE International Conference on Web Services (ICWS)

作者： Li Yusheng Haihong, E. Song Meina Song Junde Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781467372725

In a recommender system, items can be rated across multiple fields by users with varying degrees of familiarity. Hence, the ratings in a recommender system should have different recommended weights. Ratings in fields where in the user has high or low familiarity should be given high or low recommended weights, respectively. However, current recommendation algorithms ignore this problem and use the ratings indiscriminately, thus affecting the accuracy of the recommendation system. In this paper, we provide a focused study of user-familiarity degree-aware recommendation and develop a user-familiarity degree-aware latent factor model for recommendations that considers both user familiarity and item features reflected by the tagging information. We also design a user-familiarity degree-aware probability matrix factorization model, which computes the degree of familiarity of a user with the items he/she has rated. By using the user-familiarity degree, different recommended weights are given to every rating to obtain precise recommendations. The experiment results on real-world datasets show that our algorithm significantly outperforms state-of-the-art latent factor models and effectively improves the accuracy of the recommendation results.

关键词： Recommender systems probability matrix factorization User-familiar degree Tagging information

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Trust-Aware Recommendation in Social Networks 1

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10th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Xiao, Yingyuan Bu, Zhongjing Hsu, Ching-Hsien Zhu, Wenxin Shen, Yan Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin Peoples R China Tianjin Univ Technol Tianjin 300384 Peoples R China Chung Hua Univ Hsinchu 30012 Taiwan

ISBN: (数字)9783319635583

ISBN: (纸本)9783319635583;9783319635576

With the popularity of online social networks, social network information is becoming increasingly important to improve recommendation effectiveness of the existing recommender systems. In this paper, we propose an improved trust-aware recommendation approach, called TRA. TRA constructs a new social trust matrix based on users' trust relationships derived from online social networks to alleviate the problem of data sparsity, and meanwhile naturally fuses users' preferences and their trusted friends' favors together by means of probability matrix factorization. The experimental results show that TRA performs much better than the state-of-the art recommendation approaches.

关键词： Recommender systems Trust-Aware recommendation Social networks Social trust matrix probability matrix factorization

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A Recommendation Algorithm Considering User Trust and Interest 17th

A Recommendation Algorithm Considering User Trust and Intere...

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17th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Mi, Chuanmin Peng, Peng Mierzwiak, Rafal Nanjing Univ Aeronaut & Astronaut Nanjing 210016 Peoples R China Poznan Univ Tech Poznan Poland

ISBN: (纸本)9783319912622;9783319912615

A traditional collaborative filtering recommendation algorithm has problems with data sparseness, a cold start and new users. With the rapid development of social network and e-commerce, building the trust between users and user interest tags to provide a personalized recommendation is becoming an important research issue. In this study, we propose a probability matrix factorization model (STUIPMF) by integrating social trust and user interest. First, we identified implicit trust relationship between users and potential interest label from the perspective of user rating. Then, we used a probability matrix factorization model to conduct matrix decomposition of user ratings information, user trust relationship, and user interest label information, and further determined the user characteristics to ease data sparseness. Finally, we used an experiment based on the Epinions website's dataset to verify our proposed method. The results show that the proposed method can improve the recommendation's accuracy to some extent, ease a cold start and solve new user problems. Meanwhile, the STUIPMF approach, we propose, also has a good scalability.

关键词： Data mining Recommender system Collaborative filtering Social trust Interest tag probability matrix factorization

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Cold-start web service recommendation using implicit feedback 26

Cold-start web service recommendation using implicit feedbac...

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26th International Conference on Software Engineering and Knowledge Engineering, SEKE 2014

作者： Tian, Gang Wang, Jian He, Keqing Zhao, Weidong Gao, Panpan State Key Laboratory of Software Engineering School of Computer Wuhan University China College of Information and Science Engineering Shandong University of Science and Technology China

Service recommendation becomes an increasingly important issue when more and more Web services are published on the Internet. Existing Web service recommendation approaches based on collaborative filtering (CF) seldom consider recommending services based on users' ratings on services since such kind of explicit feedback is difficult to collect. In addition, the new user cold-start problem is also an important issue due to the lack of accuracy in service recommendations since the new users have not yet cast a significant numbers of votes. In this paper, a dataset consisting of much user-service interaction data is reported. The interaction data created according to users' behaviors can highly represent their preferences. Therefore, we construct pseudo ratings based on this kind of implicit feedback. We develop a novel service recommendation approach which can partially deal with cold-start problem using an online learning model. Experiments show the proposed approach can achieve satisfied results in prediction accuracy and time cost. Copyright © 2014 by Knowledge Systems Institute Graduate School.

关键词： Cold-start Web service recommendation Implicit feedback probability matrix factorization

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Leveraging contextual information for cold-start Web service recommendation

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第17期31卷

作者： Tian, Gang Wang, Qibo Wang, Jian He, Keqing Zhao, Weidong Gao, Panpan Peng, Yanjun Shandong Univ Sci & Technol Shandong Prov Key Lab Wisdom Mine Informat Techno Qingdao Shandong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China

Web service recommendation becomes an increasingly important issue when more and more services are published on the Internet. Many Web service recommendation methods have been proposed in recent years, most of which adopted collaborative filtering (CF) techniques. In general, these approaches have two limitations. Firstly, they rarely leverage user ratings since this kind of explicit feedback is difficult to collect for Web services. Secondly, the new user cold-start problem is an inherent limitation of CF because the new users have not yet cast sufficient numbers of votes. In this paper, pseudo ratings of services constructed based on plenty of user-service interactions, also known as a kind of implicit feedback, are provided to represent users' preferences on services. Based on these pseudo ratings, we present a novel Web service recommendation approach, which can alleviate the cold-start problem by integrating contextual information and an online learning model. Experiments conducted on a real world data set show that, compared with the method without contextual information, our proposed approach that handles the cold start problem by integrating contextual information can achieve better F-Measure performance (5.08 times increase on average). Moreover, the proposed online recommendation approach can dramatically decrease the time overhead while keeping the similar recommendation performance.

关键词： cold-start web service recommendation implicit feedback probability matrix factorization

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