Collaborative Filtering Techniques in Recommendation Systems

Recommendation system is the tool to user preferences over a given set of items. It takes help of the previous auxiliary information in terms of feedback or ratings. The main purpose of a recommender system is to engage users and enhance their experience over the Internet. Presently, recommender systems are widely used over e-commerce and social networking sites. The different applications require specialised recommendation system for them as e-commerce sites recommendation systems are different from social networking sites. So, recommendation system’s biggest challenge is the diversity as one cannot generate an accurate prediction using the same technique for different applications. This paper is an effort to illustrate one of the popular recommendation techniques, collaborative filtering based on classes, memory based and model based on two popular data sets (Movie lens and Jester). Further, it represents a comparative analysis of how results diverge from application to application and provides a way to optimise results of existing algorithm to get most out of them. The purpose is to present an exposure and open door to use more sophisticated data mining and machine learning techniques to enhance the overall efficiency of recommendation system.

[1]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[2]  Michael J. Pazzani,et al.  Collaborative Filtering with the Simple Bayesian Classifier , 2000, PRICAI.

[3]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[4]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[5]  Kartik Hosanagar,et al.  Recommender systems and their impact on sales diversity , 2007, EC '07.

[6]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[7]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[8]  Karen Sparck Jones.  A statistical interpretation of term specificity and its application in retrieval , 1972 .

[9]  Adolf Proidl,et al.  Incorporating user control into recommender systems based on naive bayesian classification , 2007, RecSys '07.

[10]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[11]  Mamta Bhusry,et al.  Recommendation System: State of the Art Approach , 2015 .

[12]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[13]  Rushed Kanawati,et al.  A P2P Collaborative Bibliography Recommender System , 2009, 2009 Fourth International Conference on Internet and Web Applications and Services.

[14]  Lin Chen,et al.  Recommending Web Service Based on User Relationships and Preferences , 2013, 2013 IEEE 20th International Conference on Web Services.

[15]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[16]  Sutheera Puntheeranurak,et al.  An Item-based collaborative filtering method using Item-based hybrid similarity , 2011, 2011 IEEE 2nd International Conference on Software Engineering and Service Science.

[17]  Ahmed Elgohary,et al.  Wiki-rec: A semantic-based recommendation system using Wikipedia as an ontology , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[18]  George Karypis,et al.  Evaluation of Item-Based Top-N Recommendation Algorithms , 2001, CIKM '01.

[19]  Paul Resnick,et al.  Recommender systems , 1997, CACM.