Using Trust in Collaborative Filtering for Recommendations

Recommender systems are increasingly being used in e-commerce websites to solve the problem of finding right kind of information. Collaborative filtering is considered as most promising method for recommendation because it recommends items based on common interests of users. Trust Aware Recommender Systems (TARS) is an enhancement of traditional recommendation systems to improve recommendation quality which uses trusted users for recommending an item to an active user. From literature, it is proven that including all trusted users in recommendation process reduces its performance so this research work performs a filtration process on users for reduction of trusted neighborhood of an active user. The main idea of this research work is to keep only those users in trusted neighborhood whose rating behavior is similar to an active user. Subspace clustering method is used for filtration process. The proposed algorithm uses both implicit and explicit trust for trust value calculation. The results demonstrates that the proposed algorithm improves results in terms of Mean Absolute Error and Coverage as compared to other conventional methods.

[1]  William Nzoukou,et al.  A Survey Paper on Recommender Systems , 2010, ArXiv.

[2]  Martin Ester,et al.  Using a trust network to improve top-N recommendation , 2009, RecSys '09.

[3]  Kishor Sadafale,et al.  An online recommendation system for e-commerce based on apache mahout framework , 2013, SIGMIS-CPR '13.

[4]  Parham Moradi,et al.  An effective trust-based recommendation method using a novel graph clustering algorithm , 2015 .

[5]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[6]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[7]  William W. Wadge,et al.  Trust-Based Infinitesimals for Enhanced Collaborative Filtering , 2009, COMAD.

[8]  Mahdi Jalili,et al.  Recommender Systems for Social Networks Analysis and Mining: Precision Versus Diversity , 2016 .

[9]  Hans-Peter Kriegel,et al.  Exploring subspace clustering for recommendations , 2014, SSDBM '14.

[10]  Yoichi Shinoda,et al.  Information filtering based on user behavior analysis and best match text retrieval , 1994, SIGIR '94.

[11]  Daniel Thalmann,et al.  Merging trust in collaborative filtering to alleviate data sparsity and cold start , 2014, Knowl. Based Syst..

[12]  Mohsen Ramezani,et al.  Improve performance of collaborative filtering systems using backward feature selection , 2013, The 5th Conference on Information and Knowledge Technology.

[13]  Mohsen Ramezani,et al.  A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains , 2014 .

[14]  Jennifer Golbeck,et al.  Generating Predictive Movie Recommendations from Trust in Social Networks , 2006, iTrust.

[15]  Kourosh Kiani,et al.  A new method to find neighbor users that improves the performance of Collaborative Filtering , 2017, Expert Syst. Appl..

[16]  Mahdi Jalili,et al.  Recommender systems based on collaborative filtering and resource allocation , 2014, Social Network Analysis and Mining.

[17]  Neil Yorke-Smith,et al.  Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems , 2015, Knowl. Based Syst..

[18]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[19]  Yang Guo,et al.  Bayesian-Inference-Based Recommendation in Online Social Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

[20]  Chen Meng,et al.  A Method to Solve Cold-Start Problem in Recommendation System based on Social Network Sub-community and Ontology Decision Model , 2013, ICMT 2013.

[21]  J. Lewis,et al.  Trust as a Social Reality , 1985 .

[22]  Ramanathan V. Guha,et al.  Propagation of trust and distrust , 2004, WWW '04.

[23]  John Riedl,et al.  Learning preferences of new users in recommender systems: an information theoretic approach , 2008, SKDD.

[24]  Huan Liu,et al.  mTrust: discerning multi-faceted trust in a connected world , 2012, WSDM '12.

[25]  Punam Bedi,et al.  Trust based recommender system using ant colony for trust computation , 2012, Expert Syst. Appl..

[26]  Hyung Jun Ahn,et al.  A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..

[27]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[28]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[29]  Yunkyoung Lee,et al.  RECOMMENDATION SYSTEM USING COLLABORA TIVE FILTERING , 2015 .

[30]  Korris Fu-Lai Chung,et al.  An empirical study of a cross-level association rule mining approach to cold-start recommendations , 2008, Knowl. Based Syst..

[31]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[32]  Chris Cornelis,et al.  Trust- and Distrust-Based Recommendations for Controversial Reviews , 2011, IEEE Intelligent Systems.

[33]  Pier Luca Lanzi,et al.  A novel intuitionistic fuzzy clustering method for geo-demographic analysis , 2012, Expert Syst. Appl..

[34]  Parham Moradi,et al.  A trust-aware recommendation method based on Pareto dominance and confidence concepts , 2017, Knowl. Based Syst..

[35]  N. Sahli,et al.  Trust-Aware Recommender Systems for Open and Mobile Virtual Communities , 2010 .

[36]  J. H. Davis,et al.  An Integrative Model Of Organizational Trust , 1995 .

[37]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[38]  Ruimin Shen,et al.  A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity , 2008, ECML/PKDD.

[39]  Le Hoang Son,et al.  Spatial interaction - modification model and applications to geo-demographic analysis , 2013, Knowl. Based Syst..

[40]  Le Hoang Son,et al.  An application of fuzzy geographically clustering for solving the Cold-Start problem in recommender systems , 2013, 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR).

[41]  Konstantinos G. Margaritis,et al.  COLLABORATIVE FILTERING ENHANCED BY DEMOGRAPHIC CORRELATION , 2004 .

[42]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[43]  Parham Moradi,et al.  A reliability-based recommendation method to improve trust-aware recommender systems , 2015, Expert Syst. Appl..

[44]  Jie Lu,et al.  An effective recommender system by unifying user and item trust information for B2B applications , 2015, J. Comput. Syst. Sci..

[45]  Usman Qamar,et al.  Improving collaborative filtering by selecting an effective user neighborhood for recommender systems , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[46]  Mohammad Daoud,et al.  An Item-Oriented Algorithm on Cold-Start Problem in Recommendation System , 2015 .