Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering

Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering

Dr. Arunesh Associate Professor of Computer Science

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Gopinath Ganapathy

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Bharathidasan University

Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering

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S Ray,A Mahanti Filler Items Strategies for Effective Shilling Attacks.
Bhaskar Mehta,Wolfgang Nejdl (2008). Attack resistant collaborative filtering.
B Mobasher,R Bruke,R Bhaumik,C Williams (2007). Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness.
S Zhang,A Chakrabarti,J Ford,F Makedon (2006). Attack Detection in Time Series for Recommender Systems.
Paul-Alexandru Chirita,Wolfgang Nejdl,Cristian Zamfir (2005). Preventing shilling attacks in online recommender systems.
P Resnick,N Lacovou,M Suchak,J Bergstrom,Riedl (1994). GroupLens: A open architecture for Collaborative filtering of netnews.
M O'mahony,N Hurley,G Silvestre (2005). Recommender Systems: Attack Types and Strategies.
Sean Mcnee,John Riedl,Joseph Konstan (2006). Being accurate is not enough.
Shyong Lam,John Riedl (2004). Shilling recommender systems for fun and profit.
V Krishnan,P Narayanashetty,M Nathan,R Davies,J Konstan (2008). Who Predicts Better?-Results from an Online Study Comparing Humans and an Online recommender System.
Jennifer Golbeck,Bijan Parisa,James Hendler (2005). Trust Networks on the Semantic Web.
G Ganapathy,K Arunesh (2009). Recommendation System Framework based on Web Usage Mining IJAM.
G Ganapathy,K Arunesh (2010). Feature Analysis of Recommender Techniques Employed in the recommender engines.
C Williams,B Mobasher (2007). Defending recommender systems : detection of profile injection attacks.
Benjamin Van Roy,Xiang Yan (2010). Manipulation Robustness of Collaborative Filtering.
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Bhaskar Mehta,Wolfgang Nejdl (2007). Unsupervised strategies for shilling detection and robust collaborative filtering.
B Mobasher,R Burke,J Sandvig Model-Based Collaborative as a Defense Against Profile Injection Attacks.
M Mahony,N Hurley,G Silvestre Recommender Systems: Attack Types and Strategies.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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How to Cite This Article

Dr. Arunesh. 1970. \u201cPredictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering\u201d. Unknown Journal GJCST Volume 11 (GJCST Volume 11 Issue 11).

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GJCST Volume 11 Issue 11

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Submission ReceivedApril 17, 2011
Peer Review Double Blind
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Accepted May 9, 2011
Published May 22, 2011

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July 6, 2011

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Total Score: 107

Country: Unknown

Subject: Uncategorized

Authors: Dr. Arunesh,Gopinath Ganapathy (PhD/Dr. count: 1)

View Count (all-time): 191

Total Views (Real + Logic): 20750

Total Downloads (simulated): 11057

Publish Date: 1970 01, Thu

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Total Views: 20750

Total Downloads: 11057

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