Digital Libraries: Information Broker Roles in Collaborative Filtering

2014 ◽

Vol 1044-1045 ◽

pp. 1484-1488

Author(s):

Yue Kun Fan ◽

Xin Ye Li ◽

Meng Meng Cao

Keyword(s):

Collaborative Filtering ◽

Social Relationships ◽

Digital Libraries ◽

Social Relationship ◽

Nearest Neighbor ◽

Mean Absolute Error ◽

Absolute Error ◽

Personalized Recommendation ◽

Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

Download Full-text

COLLABORATIVE FILTERING: A NEW APPROACH TO SEARCHING DIGITAL LIBRARIES

New Review of Information Networking ◽

10.1080/13614570500054229 ◽

2004 ◽

Vol 10 (2) ◽

pp. 177-191 ◽

Cited By ~ 7

Author(s):

Janet Webster ◽

Seikyung Jung ◽

Jon Herlocker

Keyword(s):

Collaborative Filtering ◽

Digital Libraries ◽

New Approach

Download Full-text

A hybrid approach for article recommendation in research social networks

Journal of Information Science ◽

10.1177/0165551517728449 ◽

2017 ◽

Vol 44 (5) ◽

pp. 696-711 ◽

Cited By ~ 2

Author(s):

Jianshan Sun ◽

Yuanchun Jiang ◽

Xusen Cheng ◽

Wei Du ◽

Yezheng Liu ◽

...

Keyword(s):

Social Networks ◽

Social Network ◽

Social Networking ◽

Collaborative Filtering ◽

Digital Libraries ◽

User Study ◽

Hybrid Approach ◽

Quality Information ◽

Complex Task ◽

Data Sparsity

With the prevalence of research social networks, determining effective methods for recommending scientific articles to online scholars has become a challenging and complex task. Current studies on article recommendation works are focused on digital libraries and reference sharing websites while studies on research social networking websites have seldom been conducted. Existing content-based approaches or collaborative filtering approaches suffer from the problem of data sparsity. The quality information of articles has been largely ignored in previous studies, thus raising the need for a unified recommendation framework. We propose a hybrid approach to combine relevance, connectivity and quality to recommend scientific articles. The effectiveness of the proposed framework and methods is verified using a user study on a real research social network website. The results demonstrate that our proposed methods outperform baseline methods.

Download Full-text

Enhancing Item-Based Collaborative Filtering with Item Correlations for Music Recommendation System

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a2016.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 1548-1553

Keyword(s):

Collaborative Filtering ◽

Digital Libraries ◽

Correlation Matrix ◽

Recommendation System ◽

Pearson Correlation ◽

Recommendation Systems ◽

Vital Role ◽

Music Recommendation ◽

Filtering Method ◽

Music Recommendation System

Music recommendation systems are playing a vital role in suggesting music to the users from huge volumes of digital libraries available. Collaborative filtering (CF) is a one of the well known method used in recommendation systems. CF is either user centric or item centric. The former is known as user-based CF and later is known as item-based CF. This paper proposes an enhancement to item-based collaborative filtering method by considering correlation among items. Lift and Pearson Correlation coefficient are used to find the correlation among items. Song correlation matrix is constructed by using correlation measures. Proposed method is evaluated on the benchmark dataset and results obtained are compared with basic item-based CF

Download Full-text

Interest-Based User Grouping Model for Collaborative Filtering in Digital Libraries

Digital Libraries: International Collaboration and Cross-Fertilization - Lecture Notes in Computer Science ◽

10.1007/978-3-540-30544-6_61 ◽

2004 ◽

pp. 533-542 ◽

Cited By ~ 6

Author(s):

Seonho Kim ◽

Edward A. Fox

Keyword(s):

Collaborative Filtering ◽

Digital Libraries ◽

User Grouping

Download Full-text

A PageRank-based collaborative filtering recommendation approach in digital libraries

Tehnicki vjesnik - Technical Gazette ◽

10.17559/tv-20160602011232 ◽

2017 ◽

Vol 24 (4) ◽

Keyword(s):

Collaborative Filtering ◽

Digital Libraries

Download Full-text

A Collaborative Filtering Based Re-ranking Strategy for Search in Digital Libraries

Digital Libraries: Implementing Strategies and Sharing Experiences - Lecture Notes in Computer Science ◽

10.1007/11599517_23 ◽

2005 ◽

pp. 194-203 ◽

Cited By ~ 9

Author(s):

U. Rohini ◽

Vamshi Ambati

Keyword(s):

Collaborative Filtering ◽

Digital Libraries ◽

Ranking Strategy

Download Full-text

The prospects for IRBIS development: The new product - IRBIS64+

Scientific and Technical Libraries ◽

10.33186/1027-3689-2017-11-65-74 ◽

2017 ◽

pp. 65-74 ◽

Cited By ~ 1

Author(s):

Alexander Brodovsky ◽

Konstantin Sboichakov ◽

Vladimir Sokolovsky

Keyword(s):

Digital Libraries ◽

New Product ◽

Automation System ◽

Library Automation ◽

Expanded Access ◽

Access Right

IRBIS64+ - the new product of IRBIS Library Automation System designed for building and maintaining digital libraries, is introduced. IRBIS64+ new functionality is revealed. New possibilities for users, including those with expanded access right, are described. The IRBIS64+ modules are named.

Download Full-text