Digital Libraries: Information Broker Roles in Collaborative Filtering

1998 ◽  
pp. 679-680
Author(s):  
Annika Waern ◽  
Mark Tierney ◽  
Åsa Rudström ◽  
Jarmo Laaksolahti ◽  
Torben Mård
Keyword(s):  
Collaborative Filtering ◽  
Digital Libraries ◽  
Information Broker

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

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.

scholarly journals COLLABORATIVE FILTERING: A NEW APPROACH TO SEARCHING DIGITAL LIBRARIES

2004 ◽  
Vol 10 (2) ◽  
pp. 177-191 ◽  
Author(s):  
Janet Webster ◽  
Seikyung Jung ◽  
Jon Herlocker
Keyword(s):  
Collaborative Filtering ◽  
Digital Libraries ◽  
New Approach

A hybrid approach for article recommendation in research social networks

2017 ◽  
Vol 44 (5) ◽  
pp. 696-711 ◽  
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.

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

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

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

2017 ◽  
pp. 65-74 ◽  
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.

Digital Libraries

2011 ◽  
Vol 3 (2) ◽  
pp. 161-162
Author(s):  
Amitkumar Lalitbhai Ghoricha ◽  
Keyword(s):  
Digital Libraries
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