A New Algorithm for Performing Ratings-Based Collaborative Filtering

Yang, Fanchang; Zhu, Yu-Xian; Shi, Bao‐Sen

doi:10.1007/3-540-36901-5_25

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Real-time problem with require the online services a substantial increase in the customer becomes more and more prominent, effective way to solve this problem is to use distributed computing technology. In addition, project-based clustering algorithm for collaborative filtering [4] can significantly reduce the nearest neighbor of the query space, in order to effectively solve largescale data processing system recommended facing real issues.…”

Section: ) Real-time Problemmentioning

confidence: 99%

An Improved Personalized Recommendation System Research

2013

AMR

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In order to find information of interest and found valuable information resources in enrich Internet data. This paper describes a personalized recommendation system, personalized recommendation system is an intelligent recommendation system to help e-commerce site for customers to provide complete personalized shopping decision support and information services. for the User Rating data extreme sparseness, This paper presents nearest neighbor collaborative filtering algorithm based on project score predicted ,experiments show that this method can improve the quality of recommendation system.

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Section: ) Real-time Problemmentioning

confidence: 99%

An Improved Personalized Recommendation System Research

2013

AMR

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“…In addition, project-based clustering algorithm for collaborative filtering [4] can significantly reduce the nearest neighbor of the query space, in order to effectively solve large-scale data processing system recommended facing real issues.…”

Section: ) Real-time Problemmentioning

confidence: 99%

An Improved Personalized Recommendation System Research

Li¹,

Li²

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-In order to find information of interest and found valuable information resources in enrich Internet data. This paper describes a personalized recommendation system, personalized recommendation system is an intelligent recommendation system to help e-commerce site for customers to provide complete personalized shopping decision support and information services. for the User Rating data extreme sparseness, This paper presents nearest neighbor collaborative filtering algorithm based on project score predicted ,experiments show that this method can improve the quality of recommendation system.

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Using element and document profile for information clustering

Lai

Soh

2004

IEEE International Conference on E-Technology, E-Commerce and E-Service, 2004. EEE '04. 2004

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The tremendous growth in the amount of information available and the number of visitors to web sites in the recent years poses some key challenges for information filtering and retrieval. Web visitors not only expect high quality and relevant information, but also wish that the information be presented in an as efficient way as possible. The traditional filtering methods, however, only consider the relevant values of document. These conventional methods fail to consider the efficiency of documents retrieval. In this paper, we propose a new algorithm to calculate an index called document similarity score based on elements of the document.Using the index, document profile will be derived. Any documents with the similarity score above a given threshold will be clustered. Using these pre-clustered documents, information filtering and retrieval can be made more efficient.

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A New Algorithm for Performing Ratings-Based Collaborative Filtering

Cited by 6 publications

References 15 publications

An Improved Personalized Recommendation System Research

An Improved Personalized Recommendation System Research

An Improved Personalized Recommendation System Research

Using element and document profile for information clustering

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