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检索条件"主题词=Recommendation algorithm"

共 370 条记录，以下是331-340 订阅

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Design of the Tourism-information-service-oriented Collaborative Filtering recommendation algorithm

Design of the Tourism-information-service-oriented Collabora...

引用

The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Chen Shan Zhang Mu Luo Jing Feng Lei GIS & Tourism IT Laboratory Shenzhen Tourism College of Jinan University Shenzhen Guangdong Provinc Business English Department Shenzhen Tourism College of Jinan University Shenzhen Guangdong Provinc Tourism Management Department Shenzhen Tourism College of Jinan University Shenzhen Guangdong Provi

ISBN: (纸本)9781424472352

Personalized recommendation technology is one key application in modern Electronic commerce field with optimistic prospect. As the urgency of the use of recommendation technology in tourism industry, the authors try to design a collaborative filtering recommendation algorithm integrating with nearest neighbor recommendation and cluster analysis referring to national criteria "Classification, Investigation and Evaluation of Tourism Resource "on the base of existing collaborative filtering recommendation technology. Theoretical mechanism and realization method of this improved collaborative filtering recommendation algorithm will be also discussed.

关键词： recommendation algorithm collaborative filtering tourism information service

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Rating Prediction using Category Weight Factorization Machine in Bigdata Environment

Rating Prediction using Category Weight Factorization Machin...

引用

International Conference on Communication Workshop

作者： Yu Zhao Khalil Mansouri Yang Yang Zhenqiang Mi School of Computer and Communication Engineering University of Science and Technology Beijing China

Rating Prediction is a key problem in recommendation system, especially in Bigdata environment with data sparsity. Recently, Factorization Machine (FM) has been proven to be effective in solving the recommendation problem. Whereas, valuable category information of users and items are neglected in basic FM model. In this paper, we fully explore the capabilities of category information to improve the accuracy of rating prediction, and proposed a Category Weight Factorization Machine (CW-FM) based on FM. CW-FM utilizes hierarchical category information to avoid the interaction between feature vectors which have the subordinate relations. Combined with user and item category information, CW-FM is proven to be an effective solutions to reducing the rating error in recommendation systems. The proposed CW-FM is evaluated by extensive experiments with real world datasets. Results show that CW-FM model achieves better iterative efficiency and higher rating accuracy compared to contemporary schemes.

关键词： factorization machine rating prediction recommendation algorithm category weights cold start

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

Intelligent prediction and recommendation optimization metho...

引用

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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The Research on Collaborative Filtering in Personalization recommendation System

The Research on Collaborative Filtering in Personalization R...

引用

International Conference on Mechatronics and Semiconductor Materials (ICMSCM 2013)

作者： Han, Dan Liu, Bing Sun, Yan Air Force Aviat Univ Dept Comp Changchun 130022 Peoples R China

This paper does a performance comparison and evaluation to the CF algorithm based on the cosine similarity, the correlation similarity and project rating, and analyzes and researches its application, facing problems, ... 详细信息

ISBN: (纸本)9783037859391

关键词： Personalization recommendation system Collaborative filtering Similarity recommendation algorithm MAE (mean absolute error)

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A Collaborative Filtering recommendation Approach Based on User Rating Similarity and User Attribute Similarity

A Collaborative Filtering Recommendation Approach Based on U...

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International Conference on Mechatronics and Semiconductor Materials (ICMSCM 2013)

作者： Ge, Feng Ningbo City Coll Vocat Technol Ningbo 315100 Zhejiang Peoples R China

ISBN: (纸本)9783037859391

With the speedy development of network, information technology has provided an unmatched amount of information resources. It has also led to the problem of information overload. However, people experiences and knowledge often do not enough to process the vast amount of usable information. Thus, approaches to help find resources of interest have attracted much attention from researchers. And recommender systems have arrived to solve this problem. Recommender system plays an important role mainly in an electronic commerce environment as a new marketing strategy. Although a varied of recommendation techniques has been developed recently, collaborative filtering has been known to be the most successful recommendation techniques and has been used in a number of different applications. But traditional collaborative filtering recommendation algorithm has the problem of sparsity. Aiming at the problem of data sparsity for personalized filtering systems, a collaborative filtering recommendation algorithm based on user rating similarity and user attribute similarity is given. This approach not only considers the user item rating information, but also takes into account the user attribute.

关键词： collaborative filtering recommendation algorithm user rating similarity user attribute similarity

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An algorithm for Routes recommendation Service Based on the Radio-Frequency Identification Application

An Algorithm for Routes Recommendation Service Based on the ...

引用

Joint World Congress of the International-Fuzzy-Systems-Association (IFSA) / Annual Meeting of the North-American-Fuzzy-Information-Processing-Society (NAFIPS)

作者： Zhao, Yang Gao, Xuedong Wu, Sen Univ Sci & Technol Beijing Dongling Sch Econ & Management Beijing 100083 Peoples R China

ISBN: (纸本)9781479903481

Creating high quality products/services in terms of consumer preference has become a critical issue for tourism managers. To fulfil these needs, this paper proposes a route recommendation algorithm that supplies movement routes with the locations they want visit. The analysis of visiting locations and corresponding timestamps are based on a Radio-Frequency Identification (RFID) data and stored in a route database. We firstly proposed a point of interest (POI) Sequence of RFID Probability Events Mining algorithm to generate the POI Sequence. According to the visitor's route recommendation service requirement, based on the POI Sequence, we develop a recommendation of Route based on POI Sequence algorithm. A simulation case is implemented to show the feasibility of the proposed algorithm. Based on the experimental results, it is clear that the recommended route satisfies visitor requirements using previous visitor's preference.

关键词： recommendation algorithm RFID point of interest route recommendation

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The Enhancement and Application of Collaborative Filtering in e-Learning System 5

The Enhancement and Application of Collaborative Filtering i...

引用

5th International Conference on Swarm Intelligence (ICSI)

作者： Song, Bo Gao, Jie Shenyang Normal Univ Coll Software Shenyang 110034 Liaoning Provin Peoples R China

ISBN: (纸本)9783319118970;9783319118963

Collaborative Filtering recommendation algorithm is one of the most popular approaches for determining recommendations at present and it can be used to solve Information Overload issue in e-Learning system. However the Cold Start problem is always one of the most critical issues that affect the performance of Collaborative Filtering recommender system. In this paper an enhanced composite recommendation algorithm based on content recommendation tags extracting and CF is proposed to make the CF recommender system work more effectively. The final experiment results show that the new enhanced recommendation algorithm has some advantages on accuracy compared with several existing solutions to the issue of Cold Start and make sure that it is a feasible and effective recommendation algorithm.

关键词： recommendation algorithm Cold Start Collaborative Filtering e-Learning

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Attribute Clustering based Collaborative Filtering

Attribute Clustering based Collaborative Filtering

引用

International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Zhang Liu-mei Ma Jian-feng Lu Di Wang Yi-chuan Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9783038350125

The paper proposed an attribute clustering based collaborative filtering algorithm for recommendation. It utilizes similarity to filter out redundant attributes by feature selection. Then by incorporating K-Means clustering, it is able to effectively solve the rating scale problems existing in the traditional collaborative filtering recommendation algorithm. The algorithm is verified by real data sets. Experiments use location information for clustering the restaurant data. By integration of users rating on restaurant service and external impression the experiment study combined the collaborative filtering philosophy to provide recommendation service for users. Experimental results show that compared with the item rating based recommended algorithm, the algorithm has ideal recommended quality and improved accuracy, and then it has reduced the data sparsity.

关键词： recommendation algorithm Attributes Clustering Score Predicts Feature Selection

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Random Walks in Recommender Systems: Exact Computation and Simulations 23

Random Walks in Recommender Systems: Exact Computation and S...

引用

23rd International Conference on World Wide Web (WWW)

作者： Cooper, Colin Lee, Sang Hyuk Radzik, Tomasz Siantos, Yiannis Kings Coll London Dept Informat London England

ISBN: (纸本)9781450327459

A recommender system uses information about known associations between users and items to compute for a given user an ordered recommendation list of items which this user might be interested in acquiring. We consider ordering rules based on various parameters of random walks on the graph representing associations between users and items. We experimentally compare the quality of recommendations and the required computational resources of two approaches: (i) calculate the exact values of the relevant random walk parameters using matrix algebra;(ii) estimate these values by simulating random walks. In our experiments we include methods proposed by Fouss et al. [7, 8] and Gori and Pucci [10], method P-3, which is based on the distribution of the random walk after three steps, and method P-alpha(3), which generalises P-3. We show that the simple method P-3 can outperform previous methods and method P-alpha(3) can offer further improvements. We show that the time- and memory-efficiency of direct simulation of random walks allows application of these methods to large datasets. We use in our experiments the three MovieLens datasets.

关键词： recommendation system recommendation algorithm random walk

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Development of a hybrid recommendation approach based on item content and user social influence

Development of a hybrid recommendation approach based on ite...

引用

2014 International Conference on Information Management and Management Engineering (IMME 2014)

作者： Na Chang Takao Terano Tokyo Institute of Technology

With the development of E-commerce and social media, personalized recommendation service becomes more and more important to both companies and users. In this paper, we propose a hybrid recommendation approach based on items' content and users' social influence. On the basis of social influence network and opinion formation models, we suggest that a user's preference for an item is the combination of his/her own consideration for the item, direct influence imposed by the neighborhood, and indirect influence exerted by public opinion through social interactions. Furthermore, we distinguish direct influence into persuasive influence and supportive influence and discuss the effectiveness to take into account the combined effect of these two types of influence.

关键词： recommendation algorithm social influence supportive influence persuasive influence

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