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proc 2014 inter conf contemporary comp informatics (ic3i)

作者： Ghuli, Poonam Ghosh, Atanu Shettar, Rajashree RV Coll Engn Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781479966295

The tremendous increase in information available over the Internet has created a challenge in searching of useful information, therefore intelligent approaches are needed to provide users to efficiently locate and retrieve information from the Web. In recent times recommender systems, recommend everything from movies, books, music, restaurant, news to jokes. collaborative filtering (CF) algorithms are one of the most successful recommendation techniques which present information on items and products that are according to user's interest. There are two methods in CF, user-based CF and item-based CF. Former finds a certain user's interests by finding other users who have similar interests whereas item based CF looks into a set of items rated by all users and computes how similar they are to the target item under recommendation. This paper aims to develop a model by splitting the costly computations in CF algorithms into three Map-Reduce phases. Further, each of these phases can be executed independently on different nodes in parallel. To compute the similarity, the Pearson correlation algorithm is used, which measures the how two items linearly relate to each other, giving a value between -1 and +1 inclusive. In addition, this paper compares the implementation of item based and user based CF algorithm on map-reduce framework. Experimental results showed that the running time of the algorithm improves by approximately 30% with every addition of a node, into a Hadoop cluster. However, item-based CF showed better scalability than userbased CF.

关键词： collaborative filtering algorithm Hadoop HDFS mapReduce recommender system

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A Neural Networks-based Clustering collaborative filtering algorithm in E-commerce Recommendation System

A Neural Networks-based Clustering Collaborative Filtering A...

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International Conference on Web Information Systems and Mining

作者： Mai, Jianying Fan, Yongjian Shen, Yanguang Artillery Command Acad PLA China Chengdu Peoples R China Hebei Univ Engn Hebei Peoples R China

ISBN: (纸本)9780769538174

E-commerce recommendation system is one of the most important and the most successful application field of data mining technology. Recommendation algorithm is the core of the recommendation system. In this paper, a neural networks-based clustering collaborative filtering algorithm in e-commerce recommendation system is designed,trying to establish an classifier model based on BP neural network for the pre-classification to items and giving realization of clustering collaborative filtering algorithm and BP neural network algorithm, and carring on the analysis and discussion to this algorithm from multiple aspects. This algorithm is helpful to improve sparsity problem of collaborative filtering algorithm and to form the more effective and the more accurate recommendation result.

关键词： e-commerce recommendation system neural networks clustering algorithm collaborative filtering algorithm recommendation algorithm

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DATA SENSITIVE RECOMMENDATION BASED ON COMMUNITY DETECTION

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2015年第2期40卷 143-159页

作者： Su, Chang Yu, Yue Xie, Xianzhong Wang, Yukun Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Mobile Commun Technol Chongqing Peoples R China

collaborative filtering is one of the most successful and widely used recommendation systems. A hybrid collaborative filtering method called data sensitive recommendation based on community detection (DSRCD) is proposed as a solution to cold start and data sparsity problems in CF. Data sensitive similarity is combined with Pearson similarity to calculate the similarity between users. alpha is the control parameter. A predicted rating mechanism is used to solve data sparsity problem and to obtain more accurate recommendation. Both user-user similarity and item-item similarity are considered in predicted rating mechanism. beta is the control parameter. Moreover, in the constructed K-nearest neighbour set, both user-community similarity and user-user similarity are considered. The target user is either in the community or has some correlation to the community. Calculating the user-community similarity can cope with cold start problem. To calculate the recommendation, movielens data sets are used in the experiments. First, parameters alpha and beta are tested and DSRCD is compared with traditional collaborative filtering recommendation algorithm (TCF) and Zhao's algorithm. DSRCD always has better results than TCF. When K = 30, we have better performance results than Zhao's algorithm.

关键词： Community detection collaborative filtering algorithm Cold start Predicted rating mechanism

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A Rough Set-based Clustering collaborative filtering algorithm in E-commerce Recommendation System

A Rough Set-based Clustering Collaborative Filtering Algorit...

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2nd International Conference on Information Management, Innovation Management and Industrial Engineering

作者： Fan, Yongjian Mai, Jianying Ren, Xiaofei Hebei Univ Engn Handan Hebei Peoples R China Artillery Command Acad PLA China Nanjing Peoples R China

ISBN: (纸本)9780769538761

Rough set is a new mathematical tool that deal with incomplete and uncertain knowledge, it can improve the classification accuracy because of its characteristics. Recommendation algorithm is the core of the recommendation system. In this paper, a rough set-based clustering collaborative filtering algorithm in e-commerce recommendation system is designed. This paper try to establish an classifier model based on rough set for the pre-classification to items and give realization of clustering collaborative filtering algorithm and procedure of rough set algorithm, and carry on the analysis and discussion to this algorithm from multiple aspects. This algorithm is helpful to improve sparsity problem of collaborative filtering algorithm and to form the more effective and the more accurate recommendation results.

关键词： rough set clustering algorithm data mining collaborative filtering algorithm recommendation algorithm

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An Optimization Method For Recommendation System Based On User Implicit Behavior

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

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2015 Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

作者： Peng Yi Cheng Yang Chen Li Meng Chen Information Engineering School Communication University of China Beijing China

ISBN: (纸本)9781467377249

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

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A New Team Recommendation Model with Applications in Social Network

A New Team Recommendation Model with Applications in Social ...

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IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Hong, Shaowen Mao, Chengjie Yang, Zhenxiong Lai, HanJiang S China Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479937769

Nowadays, some users with similar interests or same tasks may form a cooperative team in the social network, where resources and information can be wildly shared. With more and more users joining in the social network, the number of teams is growing up at the same time. How to recommend a team in which a user is interested has become a new and important topic in the social network. The purpose of this paper is to propose a novel model for team recommendation to help users find interesting teams in social network,which is using collaborative filtering algorithm and trust propagation theory. Formally, we construct team recommendation lists from three steps. Firstly, it computes the team similarity behavior between the user and his/her friends and get the first recommendation team list from it. Secondly, it gets the team recommendation list from the user's latent friends list. Finally, the third list is got from the hot teams by applied weighting factor theory. We obtain the data from a social network called schol@t (***) to make experimental analysis, which demonstrate that the team recommendation model is effective.

关键词： social network team recommendation model trust propagation collaborative filtering algorithm

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Intelligent prediction and recommendation optimization method based on fuzzy clustering and time weighting 2018

Intelligent prediction and recommendation optimization metho...

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20th International Conference on Information Integration and Web-Based Applications and Services (iiWAS)

作者： Jiang Wei-Jin Chen Jia-Hui Xu Yu-Hui Hunan Univ Commerce Inst Big Data & Internet Innovat Changsha Hunan Peoples R China

ISBN: (纸本)9781450364799

In view of the problem of data overload on the Internet platform, it is increasingly difficult for users to dig out useful and useful information. Many scholars have proposed various recommendation systems, but when calculating user similarity, traditional collaborative filtering recommendation algorithms often only Consider a single user scoring matrix, ignoring the impact of correlations between projects on recommendation accuracy. Therefore, an improved model of collaborative filtering recommendation is presented in this paper. Firstly, a method of item similarity measurement is introduced in the process of computing the user's nearest neighbor to get more appropriate neighbors. In addition, due to that the users interests will decay over time, time weight is added in the process of computing item ratings. Experimental results show that the proposed algorithm can obtain better performance than other traditional collaborative filtering algorithms in aspects of prediction accuracy and classification accuracy.

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Research on music push model based on terminal situation model

Research on music push model based on terminal situation mod...

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2014 International Conference on Engineering Management and Industrial Engineering（EMIE2014)

作者： H.Y.Han S.H.Guo Y.Han School of Information Central University of Finance and Economics

The main current information push model is based on the users' interest, lacking theoretical support to terminal situation model. To improve the approach of pushing music, realizing mobiletermination-oriented pattern, this paper presents a music push model that focuses on terminal situation. Firstly, the paper defines the concept of terminal situation. Secondly, the paper puts forward the music push model based on Bayesian network and collaborative filtering algorithm. Next, the paper defines this model's establishment process. Finally, the paper validates the effects of terminal-situation-oriented model through living examples, indicating that this model is more applicable and effective.

关键词： Terminal situation Pushing music Bayesian network collaborative filtering algorithm Push model

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Intelligent patent recommendation system for innovative design collaboration

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2013年第6期36卷 1441-1450页

作者： Trappey, Amy J. C. Trappey, Charles V. Wu, Chun-Yi Fan, Chin Yuan Lin, Yi-Liang Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Natl Chiao Tung Univ Dept Management Sci Hsinchu Taiwan

Patents' search is increasingly critical for a company's technological advancement and sustainable marketing strategy. When most innovative designs are created collaboratively by a diverse team of researchers and technologists, patent knowledge management becomes time consuming with repeated efforts creating additional task conflicts. This research develops an intelligent recommendation methodology and system to enable timely and effective patent search prior, during, and after design collaboration to prevent potential infringement of existing intellectual property rights (IPR) and to secure new IPR for market advantage. The research develops an algorithm to dynamically search related patents in global patent databases. The system clusters users with similar patent search behaviors and, subsequently, infers new patent recommendations based on inter-cluster group member behaviors and characteristics. First, the methodology evaluates the filtered information obtained from collaborative patent searches. Second, the system clusters existing users and identifies users' neighbors based on the collaborative filtering algorithm. Using the clusters of users and their behaviors, the system recommends related patents. When collaborative design teams are planning R&D policies or searching patents and prior art claims to create new IP and prevent or settles IP legal disputes, the intelligent recommendation system identifies and recommends patents with greater efficiency and accuracy than previous systems and methods described in the literature. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Design collaboration Patent search Behavior records Patent recommendation system collaborative filtering algorithm

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Network Education Video Recommendation algorithm Based on Context and Trust Relationship

Network Education Video Recommendation Algorithm Based on Co...

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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Chenyang Zhao Junling Wang College of Information Science and Engineering Henan University of Technology School of Mathematics and Statistics Lanzhou University

ISBN: (纸本)9781467349970

With the development of information technology and the internet,there are many education videos on the network for people to study in their spare ***,it is difficult for people to choose education videos which they really *** solve the problem,a personalized recommendation algorithm based on context and trust relationship is proposed in this *** the help of this algorithm,education videos interested by users can be proactive *** algorithm improves traditional filtering recommendation *** is divided into three *** candidate video set is firstly obtained according to user-rating matrix and context,and then another set is obtained according to trust relationships between ***,the former two candidate sets are combined to determine the recommendation video *** indicate that the proposed algorithm is more accurate than traditional collaborative filtering algorithm.

关键词： personalized recommendation collaborative filtering algorithm context trust relationship education video

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