NDLI: Applying User-Favorite-Item-Based Similarity into Slope One Scheme for Collaborative Filtering

Content Provider	IEEE Xplore Digital Library
Author	Ziqing Zhang Xinhuai Tang Delai Chen
Copyright Year	2014
Description	Author affiliation: Sch. of Software, Shanghai Jiao Tong Univ., Shanghai, China (Ziqing Zhang; Xinhuai Tang) \|\| Shanghai Key Lab. of Adv. Manuf. Environ., China Telecom Shanghai Branch, Shanghai, China (Delai Chen)
Abstract	Collaborative Filtering (CF) is one of the most effective technologies in making rating prediction for recommender systems. Traditional user-based CF methods take user-item matrices as input and compute the prediction value based on user similarity. The computation of similarity between each user pair requires exact matching on item set by finding a minimum number of same items rated by both users. However in real-world scenario, the input matrix is very sparse, therefore the amount of required information is far from enough to compute reliable similarity. To deal with this so-called data sparsity problem, we introduce a novel user similarity measure based on user favorite items (UFI) and apply it into Slope One scheme for rating prediction. The experimental results suggest that UFI user similarity measure is effective in user-based approaches and UFI-based Slope One algorithm can provide better quality of prediction than best available similarity-based CF algorithms.
Starting Page	5
Ending Page	7
File Size	311042
Page Count	3
File Format	PDF
e-ISBN	9781479928774
DOI	10.1109/WCCCT.2014.43
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-27
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Slope One Accuracy Collaborative filtering Collaboration Prediction algorithms Sparse matrices Reliability User favorite items Data sparsity Recommender systems
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing

A Combined Predictor for Item-Based Collaborative Filtering

Recommend Items for User in Social Networking Services with CF

A creative personalized recommendation algorithm—User-based Slope One algorithm

A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering

Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity

Applying User Interest on Item-based Recommender System

eMenu recommender system using collaborative filtering and Slope One Predictor

Collaborative filtering methods based on user relevance degree and weights of recommend-items

Applying User-Favorite-Item-Based Similarity into Slope One Scheme for Collaborative Filtering

Similar Documents

An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing

A Combined Predictor for Item-Based Collaborative Filtering

Recommend Items for User in Social Networking Services with CF

A creative personalized recommendation algorithm—User-based Slope One algorithm

A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering

Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity

Applying User Interest on Item-based Recommender System

eMenu recommender system using collaborative filtering and Slope One Predictor

Collaborative filtering methods based on user relevance degree and weights of recommend-items

Applying User-Favorite-Item-Based Similarity into Slope One Scheme for Collaborative Filtering