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A Web service QoS prediction approach based on time- and location-aware collaborative filtering

SERVICE ORIENTED COMPUTING AND APPLICATIONS 2016年第2期10卷 135-149页

作者： Yu, Chengyuan Huang, Linpeng Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200030 Peoples R China

In QoS-based Web service recommendation, predicting quality of service (QoS) for users will greatly aid service selection and discovery. collaborative filtering (CF) is an effective method for Web service selection and recommendation. CF algorithms can be divided into two main categories: memory-based and model-based algorithms. Memory-based CF algorithms are easy to implement and highly effective, but they suffer from a fundamental problem: inability to scale-up. Model-based CF algorithms, such as clustering CF algorithms, address the scalability problem by seeking users for recommendation within smaller and highly similar clusters, rather than within the entire database. However, they are often time-consuming to build and update. In this paper, we propose a time-aware and location-aware CF algorithms. To validate our algorithm, this paper conducts series of large-scale experiments based on a real-world Web service QoS data set. Experimental results show that our approach is capable of addressing the three important challenges of recommender systems-high quality of prediction, high scalability, and easy to build and update.

关键词： Web service Qos prediction Time-aware Location-aware collaborative filtering algorithm

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A Weighted Multi-attribute Method for Personalized Recommendation in MOOCs 17

A Weighted Multi-attribute Method for Personalized Recommend...

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2nd International Conference on Crowd Science and Engineering (ICCSE) - Smart and Crowd

作者： Wang, Yuqin Liang, Bing Ji, Wen Wang, Shiwei Chen, Yiqiang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Weihai Yuanhang Technol Dev Co Ltd Weihai Shandong Peoples R China

ISBN: (纸本)9781450353755

In recent years, with the rapid development of MOOC (massive open online course), more and more people get knowledge from the Internet. The big data analysis for MOOC has become a new research direction and it has been a focus that how to recommend individually personalized videos for MOOC users. To date, the most widely used personalized recommendation technology is collaborative filtering (CF) technology. In this paper, we propose a personalized recommendation algorithm-multi-attribute weight algorithm (MAWA) based on CF. Firstly, MAWA calculates separately the weights of the attributes and attribute values of the resources for the target user. Secondly, the two weights of a video are used to get a recommendation value. Finally, the resources with N highest recommendation values are recommended for the target users. The MAWA in this paper makes up for the shortcomings of traditional CF algorithm and it can be shown from the experiment in this paper that the recall rate of MAWA is 28.3% higher than CF, which means that the recommendation results of MAWA is more accurate than those of CF. The contribution of this paper is to weight the attributes and attribute values respectively, which can reflect the users' preferences in both coarse granularity and fine granularity.

关键词： multi-attribute weight algorithm collaborative filtering algorithm MOOC video recommendation technology

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Caching Scheme Based on User Clustering and User Requests Prediction in Small Cells 17

Caching Scheme Based on User Clustering and User Requests Pr...

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IEEE 17th International Conference on Communication Technology (ICCT)

作者： Huang, Dachun Wang, Xiaoxiang Wang, Dongyu Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China

ISBN: (纸本)9781509039449

In order to cope with the relentless data tsunami brought by the growth of mobile video service, this paper proposes a two-stage caching scheme. Considering the characteristic of the ultra-dense network that many users may be in the overlapping coverage of multiple small cells, we cluster those users with k-medoids clustering algorithm and the users of the same cluster access to the same matching small cell in the first phase. In the second phase, a cache algorithm is proposed to make cache decision of video files on the basis of predicting the future requests of the users in the same small cell by collaborative filtering algorithm. At last, the simulation results show that the gain of the proposed caching scheme over the existing caching schemes in the cache hit rate and the average delay.

关键词： ultra-dense network small cell cache user clustering collaborative filtering algorithm

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AN IMPROVED HYBRID RECOMMENDATION algorithm STUDY AND DESIGN ON MAHOUT FRAMEWORK

AN IMPROVED HYBRID RECOMMENDATION ALGORITHM STUDY AND DESIGN...

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2017 2nd World Symposium on Mechanical and Control Engineering（WSMCE 2017）

作者： Dai Fei Xiaohui Cheng College of Information Science and Engineering Guilin University of Technology

collaborative filtering algorithm is one of the most widely used algorithms in the recommendation system. However, each collaborative filtering algorithm has its advantages and disadvantages. When applied to recommendation systems alone, it leads to low efficiency and low accuracy. Based on the research of existent recommendation algorithms, we improved a hybrid recommendation algorithm using Mahout Framework. This improved algorithm consists of third parts, first part: recommended results were got by using a user-based collaborative filtering algorithm to compute;second part: recommended results were got by using the improved item-based collaborative filtering algorithm to compute;third part: the two recommendation results were tested by the screening mechanism, then output the best recommended results. This paper's innovation point is that we use singular value decomposition and principal component analysis algorithm to optimize dimension reduction on item-based collaborative filtering algorithm and get better effect of the recommend system. Compared with the originally experimental results, recommendation system of the improved hybrid recommendation algorithm can have more precise outcome and high efficiency.

关键词： collaborative filtering algorithm Mahout singular value decomposition hybrid recommendation algorithm

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Caching Scheme Based on User Clustering and User Requests Prediction in Small Cells

Caching Scheme Based on User Clustering and User Requests Pr...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Dachun Huang Xiaoxiang Wang Dongyu Wang Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications

关键词： ultra-dense network small cell cache user clustering collaborative filtering algorithm

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User Interaction Analysis to Recommend Suitable Jobs in Career-Oriented Social Networking Sites 3

User Interaction Analysis to Recommend Suitable Jobs in Care...

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3rd International Conference on Data and Software Engineering (ICoDSE)

作者： Ahmed, Shibbir Hasan, Mahamudul Hoq, Md. Nazmul Adnan, Muhammad Abdullah Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh Univ Dhaka Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781509056712

Career-oriented social networking sites are very much useful for job seekers to find a suitable job and useful for recruiters as well to find the right candidate for a job. Job recommendation system helps job seekers to find appropriate jobs matching with their profile. So, it can be considered as recruiters approaching a suitable candidate whenever they have an appropriate job for them. In this paper, we present a research technique of developing a job recommendation system for the online job hunting websites to predict suitable job postings that are likely to be relevant to the user i. e., the job postings with which the users can possibly interact. Relevant jobs are those job postings on which a user may click, bookmark or reply to the recruiter. Here, we have considered all possible factors related to users as well as job items available in a publicly available partial big data set of a widely used international job hunting website. We have split the interaction data into training and test data for the purpose of evaluating our proposed system. After that we have developed an algorithm for job recommender system which can calculate similarity for useruser, item-item and hybrid of user-user and item-item perspective between training and test data set based on weighted scores using our proposed similarity computation algorithm for different jobs and users all the information present in the dataset. We have used collaborative filtering (CF) algorithm separately for user-user and item-item based appraach and for hybrid approach, we have calculated the intersection between user-user and item-item based recommended list and select top-k job items as recommended lists from the intersection. After that, we have compared the predicted recommended list based on all three approaches with the actual list and made offline evaluation of the job recommender system accuracy based on obtained score. Finally we have found that hybrid approach performs better than user-user and it

关键词： Big Data Data Mining Recommender System collaborative filtering algorithm User Interaction Analysis Social Networking Job Sites

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Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2015)

作者： Liu Xiaoping Neijiang Normal Univ Neijiang 641112 Sichuan Peoples R China

ISBN: (纸本)9781467371438

According to exploiting the users' preference for each attribute, we can get the similarity of users' interest. In this way, the similarity can be combined with the collaborative filtering algorithm which can get the users' interests from users' assessment. A new user similarity measure based on comparison model of psychology is proposed, which applies psychology theory to improve collaborative filtering, and the experimental results prove that the proposed model improves performance of collaborative filtering algorithm.

关键词： user similarity measure comparison mode collaborative filtering algorithm

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An Optimization Method For Recommendation System Based On User Implicit Behavior 5

An Optimization Method For Recommendation System Based On Us...

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Fifth International Conference on Instrumentation & Measurement, Computer, Communication, and Control (IMCCC)

作者： Yi, Peng Li, Chen Yang, Cheng Chen, Meng Commun Univ China Informat Engn Sch Beijing Peoples R China

ISBN: (纸本)9781467377232

collaborative filtering is the most worldwide and personalized video recommendation technology. As collaborative filtering recommendation system is often faced with the problem of matrix sparse on user rating. Via the introduction of the concept of collaborative filtering and the analysis of user behaviors and solution to the problem of sparse existing recommendation systems, this paper puts forward with an optimization algorithm, combining with implicit user behavior and verify the effectiveness of the optimization algorithm through the experiment.

关键词： On-line Video Recommendation System collaborative filtering algorithm User Behavior Analysis Sparsity

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The Design and Implementation of Personalized News Recommendation System 14

The Design and Implementation of Personalized News Recommend...

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14th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Han, Xuejiao Shang, Wenqian Feng, Shuchao Commun Univ China Sch Comp Sci Beijing Peoples R China Commun Univ China Sch Sci Beijing Peoples R China

ISBN: (纸本)9781479986798

With the development of network information technology, a lot of news comes into the view of Internet users. We have entered the "information overload" era. So how to find useful information becomes more and more important. Personalized news recommendation is a kind of technology to find the news that users want to get urgently. It is based on the browsing history of many users. Through analysis of their interests, we can realize the personalized news recommendation for different people. In our system, we propose an improved association rules. This method associates with collaborative filtering algorithm to form a hybrid recommendation algorithm. Through using this new algorithm, we can generate recommendation lists and realize the personalized news recommendation.

关键词： personalized news recommendation hybrid recommendation algorithm collaborative filtering algorithm association rules

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Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2009年第2期3卷 147-157页

作者： Piao, Chun-Hui Zhao, Jing Zheng, Li-Juan Renmin Univ China Sch Informat Beijing 100872 Peoples R China Shijiazhuang Railway Inst Sch Econ & Management Shijiazhuang 050043 Hebei Peoples R China Shijiazhuang Railway Inst Sch Comp & Informat Engn Shijiazhuang 050043 Hebei Peoples R China

Based on the introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the two algorithms are analyzed, and a new-entropy-based recommendation algorithm is proposed. Aiming at the drawbacks of traditional similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative filtering algorithm contributes to solving the cold-start problem and discovering users' hidden interests. Using the data selected from Movielens and Book-Crossing datasets and MAE accuracy metric, three different collaborative filtering recommendation algorithms are compared through experiments. The experimental scheme and results are discussed in detail. The results show that the entropy-based algorithm provides better recommendation quality than user-based algorithm and achieves recommendation accuracy comparable to the item-based algorithm. At last, a solution to B2B e-commerce recommendation applications based on Web services technology is proposed, which adopts entropy-based collaborative filtering recommendation algorithm.

关键词： Personalized recommendation Entropy collaborative filtering algorithm Similarity measure B2B e-commerce Web services MAE

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