NDLI: An efficient algorithm for web recommendation systems

Content Provider	IEEE Xplore Digital Library
Author	Forsati, R. Meybodi, M.R. Rahbar, A.
Copyright Year	2009
Description	Author affiliation: Department of Computer Engineering, Islamic Azad University, Karaj Branch, Iran (Forsati, R.) \|\| Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran (Meybodi, M.R.) \|\| Department of Computer Engineering, Islamic Azad University, North Branch of Tehran, Iran (Rahbar, A.)
Abstract	Different efforts have been made to address the problem of information overload on the Internet. Web recommendation systems based on web usage mining try to mine users' behavior patterns from web access logs, and recommend pages to the online user by matching the user's browsing behavior with the mined historical behavior patterns. In this paper we propose effective and scalable technique to solve the web page recommendation problem. We use distributed learning automata to learn the behavior of previous users' and cluster pages based on learned pattern. One of the challenging problems in recommendation systems is dealing with unvisited or newly added pages. As they would never be recommended, we need to provide an opportunity for these rarely visited or newly added pages to be included in the recommendation set. By considering this problem, and introducing a novel Weighted Association Rule mining algorithm, we present an algorithm for recommendation purpose. We employ the HITS algorithm to extend the recommendation set. We evaluate proposed algorithm under different settings and show how this method can improve the overall quality of web recommendations.
Starting Page	579
Ending Page	586
File Size	738994
Page Count	8
File Format	PDF
ISBN	9781424438075
DOI	10.1109/AICCSA.2009.5069385
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-10
Publisher Place	Morocco
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Itemsets Association rules Recommender systems Web pages Feedback Internet Learning automata Web server Navigation web minig web recommender system association rules data mining learning automata
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 Library 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

A dynamic web recommender system based on cellular learning automata

A Hybrid Web Recommender System Based on Cellular Learning Automata

Personalized recommendation based on WAR

Web Page Personalization Based on Weighted Association Rules

Research on Application of User Navigation Pattern Mining Recommendation

Personalized Instructing Recommendation System Based on Web Mining

A Hybrid Framework for Building a Web-Page Recommender System

Incremental FP_Growth Mining Algorithm Based on Web Information Extraction

The influence of indirect association rules on recommendation ranking lists

An efficient algorithm for web recommendation systems

Similar Documents

A dynamic web recommender system based on cellular learning automata

A Hybrid Web Recommender System Based on Cellular Learning Automata

Personalized recommendation based on WAR

Web Page Personalization Based on Weighted Association Rules

Research on Application of User Navigation Pattern Mining Recommendation

Personalized Instructing Recommendation System Based on Web Mining

A Hybrid Framework for Building a Web-Page Recommender System

Incremental FP_Growth Mining Algorithm Based on Web Information Extraction

The influence of indirect association rules on recommendation ranking lists

An efficient algorithm for web recommendation systems