A Novel Recommendation Model Based on Trust Relations and Item Ratings in Social Networks

Recommender systems play an essential role in providing users with accurate and positive items or services for their personalized preferences from large volume of information choices. Collaborative filtering (CF) is an indispensable technique of recommender systems and widely applied in many areas such as e-commerce, social medium and review sites. However, CF suffers from three issues which are cold start users, cold start items and data sparsity. These issues severely degrade the recommendation performance of CF. To address these issues, we propose TrustTR, a novel memory-based recommendation model based on trust relations and item ratings. TrustTR integrates trusted friends' recommendations, item reputation and user history ratings to improve the recommendation performance. Especially, we calculate the user reputation based on trust relations and ratings information to supply the insufficient item ratings. Thus, TrustTR can alleviate the data sparsity problem and improve the quality of recommendations for cold start users. Besides, we utilize item reputation as one of the recommendation factors to mitigate the problem of cold start items. Finally, the experimental results from a real-world dataset show that our method can produce better predictive accuracy compared with other five counterparts recommendation models.

[1]  Jing Huang,et al.  Improving the Recommendation of Collaborative Filtering by Fusing Trust Network , 2012, 2012 Eighth International Conference on Computational Intelligence and Security.

[2]  Guibing Guo,et al.  Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems , 2013, RecSys.

[3]  Le Hoang Son.  Dealing with the new user cold-start problem in recommender systems: A comparative review , 2016, Inf. Syst..

[4]  P. Santhi Thilagam,et al.  Alleviating data sparsity and cold start in recommender systems using social behaviour , 2016, 2016 International Conference on Recent Trends in Information Technology (ICRTIT).

[5]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[6]  Daniel Z. Levin,et al.  The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer , 2004, Manag. Sci..

[7]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

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

[9]  Laizhong Cui,et al.  A comprehensive trust-based item evaluation model for recommendation in social network , 2016, 2016 IEEE Symposium on Computers and Communication (ISCC).

[10]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[11]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[12]  Kai Chen,et al.  Collaborative filtering and deep learning based recommendation system for cold start items , 2017, Expert Syst. Appl..

[13]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[14]  Cécile Paris,et al.  A survey of trust in social networks , 2013, CSUR.

[15]  Jiming Liu,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Social Collaborative Filtering by Trust , 2022 .

[16]  Michael R. Lyu,et al.  Learning to recommend with social trust ensemble , 2009, SIGIR.

[17]  Mohammad Ali Abbasi,et al.  Trust-Aware Recommender Systems , 2014 .

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

[19]  Daniel Thalmann,et al.  A simple but effective method to incorporate trusted neighbors in recommender systems , 2012, UMAP.

[20]  Jun Ma,et al.  A novel recommendation approach based on users’ weighted trust relations and the rating similarities , 2016, Soft Comput..

[21]  Ee-Peng Lim,et al.  Trust-Aware Recommender Systems , 2014 .