Context-aware recommender system using trust network

Context-Aware Recommender Systems (CARS) improve traditional Recommender Systems (RS) in a wide array of domains and applications. However, CARS suffer from several inherent issues such as data sparsity and cold start. Incorporating trust into recommender systems can handle these issues. Trust-aware recommender systems use information from social networks such as trust statements, which prove another valuable information source. This paper exploits the advantages of these two systems by incorporating both trust and context information. We propose a hybrid approach: Trust based Context aware Post Filtering Approach that uses trust statements as a rich information with context compensation method of contextual post-filtering approach. Our approach utilizes the relative average difference among the context on output of trust aware collaborative filtering by incorporating explicit and implicit trust information. We also use a confidence concept to remove non-confident users from the trust network before generating prediction. The performed experiments show that the proposed approach improves upon the standard RS and outperforms recommendation approaches on real world dataset.

[1]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[2]  Jee-Hyong Lee,et al.  Improved Post-Filtering Method Using Context Compensation , 2016, Int. J. Fuzzy Log. Intell. Syst..

[3]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[4]  Paolo Bellavista,et al.  A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems , 2020, IEEE Access.

[5]  Fuzhi Zhang,et al.  Detecting shilling attacks in social recommender systems based on time series analysis and trust features , 2019, Knowl. Based Syst..

[6]  Mohsen Afsharchi,et al.  A social recommender system based on reliable implicit relationships , 2020, Knowl. Based Syst..

[7]  Bamshad Mobasher,et al.  Splitting approaches for context-aware recommendation: an empirical study , 2014, SAC.

[8]  Martin Ester,et al.  TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.

[9]  Alexander Tuzhilin,et al.  Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems , 2009, RecSys '09.

[10]  Robin Burke,et al.  Optimal Feature Selection for Context-Aware Recommendation using Differential Relaxation , 2012 .

[11]  Fateme Keikha,et al.  TB-CA: A hybrid method based on trust and context-aware for recommender system in social networks , 2015 .

[12]  Rashmi R. Sinha,et al.  Comparing Recommendations Made by Online Systems and Friends , 2001, DELOS.

[13]  Bamshad Mobasher,et al.  CARSKit: A Java-Based Context-Aware Recommendation Engine , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[14]  Jun Li,et al.  Towards Context-aware Social Recommendation via Individual Trust , 2017, Knowl. Based Syst..

[15]  Gabriel Tamura,et al.  Characterizing context-aware recommender systems: A systematic literature review , 2018, Knowl. Based Syst..

[16]  Iván Cantador,et al.  Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches , 2013, EC-Web.

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

[18]  Feifei Sun,et al.  Trust-based context-aware mobile social network service recommendation , 2017, Wuhan University Journal of Natural Sciences.

[19]  Francesco Ricci,et al.  Experimental evaluation of context-dependent collaborative filtering using item splitting , 2013, User Modeling and User-Adapted Interaction.

[20]  Parham Moradi,et al.  TCFACO: Trust-aware collaborative filtering method based on ant colony optimization , 2019, Expert Syst. Appl..

[21]  Lian Gaofeng,et al.  A Bayesian NetworkBased MethodforService Quality Optimization , 2018, Comput. Inf. Sci..

[22]  M. Abulaish,et al.  Trust and Context-based Rating Prediction using Collaborative Filtering: A Hybrid Approach , 2019, WIMS.

[23]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[24]  Meiqi Wang,et al.  Trust-Aware Collaborative Filtering with a Denoising Autoencoder , 2018, Neural Processing Letters.

[25]  Anuja Arora,et al.  Clu-PoF-A Novel Post Filtering Approach for Efficient Context Aware Recommendations , 2017, ITQM.

[26]  Sadok Ben Yahia,et al.  Context-aware recommender systems in mobile environment: On the road of future research , 2017, Inf. Syst..

[27]  M. H. Abdi,et al.  Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey , 2018, Comput. Inf. Sci..

[28]  Xin Liu.  Towards Context-Aware Social Recommendation via Trust Networks , 2013, WISE.

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

[30]  Gyu Myoung Lee,et al.  A Framework for Exploiting Internet of Things for Context-Aware Trust-Based Personalized Services , 2018, Mob. Inf. Syst..

[31]  Fadi Al-Turjman,et al.  A novel community-based trust aware recommender systems for big data cloud service networks , 2020 .

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

[33]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[34]  Jiyun Li,et al.  DTCMF: Dynamic trust-based context-aware matrix factorization for collaborative filtering , 2016, 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference.

[35]  Sambhav Yadav,et al.  Trust aware recommender system using swarm intelligence , 2018, J. Comput. Sci..

[36]  Weiguo Li,et al.  A Rating Prediction Method for Combining Social Network and Context Information , 2018 .

[37]  Bernd Ludwig,et al.  Matrix factorization techniques for context aware recommendation , 2011, RecSys '11.

[38]  Jiyun Li,et al.  TCMF: Trust-Based Context-Aware Matrix Factorization for Collaborative Filtering , 2014, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.

[39]  Francesco Ricci,et al.  Context-based splitting of item ratings in collaborative filtering , 2009, RecSys '09.

[40]  Sahin Albayrak,et al.  Inferring Contextual User Profiles - Improving Recommender Performance , 2011 .

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

[42]  Yiteng Pan,et al.  Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems , 2019, Applied Intelligence.