ABSTRACT
In social networks, trust is a fundamental notion affecting the nature and the strength of ties between individuals. It is also a piece of useful auxiliary information for improving the performance of recommendation systems. The number of ratings given by a user is minimal compared to all items in popular, widely-used e-commerce sites. Therefore, the user-item matrix that is used in collaborative filtering suffers from data sparsity, resulting in poor recommendation quality. Another issue is the cold start problem, which occurs for the inclusion of new users and new items to the system. Trust notion is helpful for alleviating the effect of these problems by providing additional relationships between the users and pointing out strong relationships. Information as to the trust between users can be explicitly available. However, such information is not widely available, and hence implicit trust models have been employed. This work analyzes two sub-problems under trust modeling for recommendation: (1) What is the relationship between explicit and implicit trust scores, are they replaceable? (2) Can we model explicit trust in a trust network? For the first problem, we present an implicit trust model and analyze the compatibility of implicit and explicit trust scores. For the second problem, we model explicit trust modeling as a link prediction problem and analyze the performance of the prediction models we generate on a set of benchmark data sets.
- Lada A Adamic and Eytan Adar. 2003. Friends and neighbors on the web. Social networks 25, 3 (2003), 211--230.Google Scholar
- Sergey Brin and Lawrence Page. 1998. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems 30, 1-7 (1998), 107--117. Google ScholarDigital Library
- Dong-Kyu Chae, Jihoo Kim, Duen Horng Chau, and Sang-Wook Kim. 2020. AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems. Association for Computing Machinery, New York, NY, USA, 1251--1260. https://doi.org/10.1145/3397271.3401038 Google ScholarDigital Library
- Jennifer Golbeck, James Hendler, et al. 2006. Filmtrust: Movie recommendations using trust in web-based social networks. In Proceedings of the IEEE Consumer communications and networking conference, Vol. 96. 282--286.Google ScholarCross Ref
- Guibing Guo, Jie Zhang, and Daniel Thalmann. 2014. Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowledge-Based Systems 57 (2014), 57--68. Google ScholarDigital Library
- Guibing Guo, Jie Zhang, Daniel Thalmann, and Neil Yorke-Smith. 2014. ETAF: An extended trust antecedents framework for trust prediction. In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014). 540--547. Google ScholarDigital Library
- Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating Collaborative Filtering Recommender Systems. ACM Trans. Inf. Syst. (Jan. 2004), 5--53. Google ScholarDigital Library
- Zarli Htun and Phyu Phyu Tar. 2013. A Trust-aware Recommender System Based on Implicit Trust Extraction. International Journal of Innovations in Engineering and Technology (IJIET) Technology(IJIET) 2, 1 (2013), 271--276.Google Scholar
- Zhengdi Hu, Guangquan Xu, Xi Zheng, Jiang Liu, Zhangbing Li, Quan Z. Sheng, Wenjuan Lian, and Hequn Xian. 2020. SSL-SVD: Semi-Supervised Learning-Based Sparse Trust Recommendation. ACM Trans. Internet Technol. 20, 1, Article 4 (Jan. 2020), 20 pages. Google ScholarDigital Library
- Mohsen Jamali and Martin Ester. 2010. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks. In Proceedings of the Fourth ACM Conference on Recommender Systems (Barcelona, Spain) (RecSys '10). Association for Computing Machinery, New York, NY, USA, 135--142. Google ScholarDigital Library
- Huan Liu Jiliang Tang, Huiji Gao and Atish Das Sarma. 2012. eTrust: Understanding Trust Evolution in an Online World. In the Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Google ScholarDigital Library
- Jebran Khan and Sungchang Lee. 2019. Implicit user trust modeling based on user attributes and behavior in online social networks. IEEE Access 7 (2019), 142826--142842.Google ScholarCross Ref
- Wonchang Lee, Yeon-Chang Lee, Dongwon Lee, and Sang-Wook Kim. 2021. Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks. Association for Computing Machinery, New York, NY, USA, 143--152. https://doi.org/10.1145/3404835.3462923 Google ScholarDigital Library
- Wentao Li, Min Gao, Wenge Rong, Junhao Wen, Qingyu Xiong, Ruixi Jia, and Tong Dou. 2017. Social recommendation using Euclidean embedding. In 2017 International Joint Conference on Neural Networks (IJCNN). 589--595.Google ScholarCross Ref
- Yakun Li, Jiaomin Liu, Jiadong Ren, and Yixin Chang. 2020. A Novel Implicit Trust Recommendation Approach for Rating Prediction. IEEE Access 8 (2020), 98305--98315.Google ScholarCross Ref
- Hyun-Kyo Oh and Sang-Wook Kim. 2017. Identifying and Exploiting Trustable Users with Robust Features in Online Rating Systems. TIIS 11, 4 (2017), 2171--2195.Google Scholar
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830. Google ScholarDigital Library
- Matthew Richardson, Rakesh Agrawal, and Pedro Domingos. 2003. Trust management for the semantic web. In International semantic Web conference. 351--368. Google ScholarDigital Library
- Fan Wang, Weiyi Zhong, Xiaolong Xu, Wajid Rafique, Zhili Zhou, and Lianyong Qi. 2020. Privacy-aware Cold-Start Recommendation based on Collaborative Filtering and Enhanced Trust. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). 655--662.Google ScholarCross Ref
- Xin Wang, Wei Lu, Martin Ester, Can Wang, and Chun Chen. 2016. Social recommendation with strong and weak ties. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 5--14. Google ScholarDigital Library
- Bo Yang, Yu Lei, Jiming Liu, and Wenjie Li. 2017. Social Collaborative Filtering by Trust. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 8 (2017), 1633--1647.Google ScholarDigital Library
- Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, and Qinyong Wang. 2019. Generating reliable friends via adversarial training to improve social recommendation. In 2019 IEEE International Conference on Data Mining (ICDM). 768--777.Google ScholarCross Ref
- Ahmed Zahir, Yuyu Yuan, and Krishna Moniz. 2019. AgreeRelTrust---A Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems. Electronics 8, 4 (2019).Google Scholar
- Chuxu Zhang, Lu Yu, Yan Wang, Chirag Shah, and Xiangliang Zhang. [n.d.]. Collaborative User Network Embedding for Social Recommender Systems. 381--389.Google Scholar
- Tong Zhao, Julian McAuley, and Irwin King. 2014. Leveraging social connections to improve personalized ranking for collaborative filtering. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 261--270. Google ScholarDigital Library
Index Terms
- Trust Modeling in Recommendation: Explicit and Implicit Trust Model Compatibility and Explicit Trust Prediction
Recommendations
Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborating filtering-based recommender systems
AbstractRecommender systems can improve the quality of life in smart cities by presenting personalized services to the community. Such systems maintain a database of user profiles for producing recommendations for a specific user. The collaborative ...
A preference elicitation method based on bipartite graphical correlation and implicit trust
In the age of big data, information overload is getting worse. Most of the existing recommender systems which apply data analysis and behavioral analysis to make personal recommendation have suffered from the problem of low prediction accuracy. To ...
Using a trust network to improve top-N recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systemsTop-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended ...
Comments