Vineet Kumar Sejwal
Muhammad Abulaish
ABSTRACT
In order to tackle the problem of information overload and effective recommendation based on users' preference, need, and interest a number of research contributions has been made for the development of recommender systems. However, certain challenges, such as data sparsity, profiling attack, and black-box recommendation still exist and hamper their prediction accuracy. In this paper, we propose a hybrid approach to predict user ratings by incorporating both trust and context of the users in traditional recommender systems using collaborative filtering method. The similarity between two users is computed using both trust value and context-based similarity. The trust value is based on three trust statements -- rating deviation, emotions, and reviews helpfulness. On the other hand, context-based similarity is based on four contextual features -- companion, place, day, and priority. The performance of the proposed trust- and context-based hybrid approach is analyzed using mean absolute error and root mean square error on a real dataset generated from two movie data sources (IMDB and Rotten Tomatoes), and it performs significantly better in comparison to some of the standard baseline methods. The rating prediction using only trust statements gives better results in comparison to other collaborative filtering approaches, such as user-based and item-based filtering approaches. Similarly, context-based collaborative filtering approach also outperforms standard collaborative filtering approaches. In addition, rating prediction using both trust- and context-based features performs better in comparison to only trust-based or context-based approaches.
- Gregory D. Abowd, Anind K. Dey, Peter J. Brown, Nigel Davies, Mark Smith, and Pete Steggles. 1999. Towards a Better Understanding of Context and Context-Awareness. In Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing. Springer, Berlin, Heidelberg, 304--307. Google Scholar
Digital Library
- Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. 2011. Context-Aware Recommender Systems. Association for the Advancement of Artificial Intelligence, 32, 3 (2011), 217--253.Google Scholar
- Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin. 2005. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. Transactions on Information Systems (TOIS) 23, 1 (2005), 103--145. Google ScholarDigital Library
- Ghazaleh Beigi, Jiliang Tang, Suhang Wang, and Huan Liu. 2016. Exploiting Emotional Information for Trust/Distrust Prediction. In Proceedings of the 16th SIAM International Conference on Data Mining (SDM' 16). SIAM, Miami, USA, 81--89.Google ScholarCross Ref
- Glenn R. Bewsell. 2012. Distrust, Fear and Emotional Learning: An Online Auction Perspective. Journal of Theoretical and Applied Electronic Commerce Research 7, 2 (2012), 1--12. Google ScholarDigital Library
- Touhid Bhuiyan, Audun Josang, and Yue Xu. 2010. Managing trust in online social networks. In Handbook of Social Network Technologies and Applications. Springer, Boston, MA, 471--496.Google Scholar
- Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender Systems Survey. Knowledge-Based Systems 46 (2013), 109--132. Google ScholarDigital Library
- Iván Cantador and Pablo Castells. 2009. Semantic Contextualisation in a News Recommender System.. In Proceedings of 1st Workshop on Context-Aware Recommender System (CARS-RecSys'09). ACM Press, New York, NY, USA.Google Scholar
- Anind K. Dey. 2001. Understanding and using Context. Personal and Ubiquitous Computing 5, 1 (2001), 4--7. Google ScholarDigital Library
- Paul Dourish. 2004. What We Talk About When We Talk About Context. Personal and Ubiquitous Computing 8, 1 (2004), 19--30.Google ScholarDigital Library
- Jennifer R Dunn and Maurice E Schweitzer. 2005. Feeling and Believing: The Influence of Emotion on Trust. Journal of Personality and Social Psychology 88, 1 (2005), 736--748.Google ScholarCross Ref
- Anindya Ghose and Panagiotis G Ipeirotis. 2011. Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics. IEEE Transactions on Knowledge and Data Engineering 23, 10 (2011), 1498--1512. Google ScholarDigital Library
- Jennifer Golbeck. 2006. Generating Predictive Movie Recommendations from Trust in Social Networks. In Proceedings of the 4th International Conference on Trust Management (i-Trust' 06). Springer, Berlin, Heidelberg, Berlin, Heidelberg, 93--104. Google ScholarDigital Library
- Jennifer A. Golbeck. 2005. Computing and Applying Trust in Web-based Social Networks. Ph.D. Dissertation. Google ScholarDigital Library
- Guibing Guo, Jie Zhang, Daniel Thalmann, Anirban Basu, and Neil Yorke-Smith. 2014. From Ratings to Trust: An Empirical Study of Implicit Trust in Recommender Systems. In Proceedings of the 29th Annual ACM Symposium on Applied Computing (SAC' 14). ACM Press, Gyeongju, Korea, 248--253. Google ScholarDigital Library
- Negar Hariri, Bamshad Mobasher, Robin Burke, and Yong Zheng. 2011. Context-Aware Recommendation based on Review Mining. In Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP--IJCAI'11). AAAI Press, Barcelona, Spain, 30--36.Google Scholar
- Chein-Shung Hwang and Yu-Pin Chen. 2007. Using Trust in Collaborative Filtering Recommendation. In Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE' 07). Springer, Berlin, Heidelberg, Kyoto, Japan, 1052--1060. Google ScholarDigital Library
- Audun Jøsang, Ross Hayward, and Simon Pope. 2006. Trust Network Analysis with Subjective Logic. In Proceedings of the 29th Australasian Computer Science Conference (ACSC ' 06), Vol. 48. ACM Press, Hobart, Australia, 85--94. Google ScholarDigital Library
- Henry Lieberman and Ted Selker. 2000. Out of context: Computer Systems that Adapt to, and Learn from, Context. IBM Systems Journal 39, 3.4 (2000), 617--632. Google ScholarDigital Library
- Paolo Massa and Paolo Avesani. 2004. Trust-aware Collaborative Filtering for Recommender Systems. In Proceedings of the OTM Confederated International Conferences "On the Move to Meaningful Internet Systems" (OTM' 04). Springer, Berlin, Heidelberg, Napa, Cyprus, 492--508.Google ScholarCross Ref
- Paolo Massa and Paolo Avesani. 2007. Trust-aware Recommender Systems. In Proceedings of the 7th International Conference on Recommender Systems (RecSys' 07). ACM Press, Minneapolis, USA, 17--24. Google ScholarDigital Library
- Paolo Massa and Paolo Avesani. 2007. Trust Metrics on Controversial Users: Balancing Between Tyranny of the Majority. International Journal on Semantic Web and Information Systems (IJSWIS) 3, 1 (2007), 39--64.Google ScholarCross Ref
- Roger C. Mayer, James H. Davis, and David F. Schoorman. 1995. An Integrative Model of Organizational Trust. Academy of Management Review 20, 3 (1995), 709--734.Google ScholarCross Ref
- John O'Donovan and Barry Smyth. 2005. Trust in Recommender Systems. In Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI ' 05). ACM Press, San Diego, California, USA, 167--174. Google ScholarDigital Library
- Ben J. Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative Filtering Recommender Systems. In The Adaptive Web. Springer, Berlin, Heidelberg, 291--324. Google ScholarDigital Library
- Ben J. Schafer, Dan Frankowski, Jonathan L. Herlocker, and Shilad Sen. 2007. Collaborative Filtering Recommender Systems. The Adaptive Web 4321 (2007), 291--324.Google ScholarCross Ref
- Guan Wang, Sihong Xie, Bing Liu, and S. Yu Philip. 2011. Review Graph Based Online Store Review Spammer Detection. In Proceedings of the 11th International Conference on Data Mining (ICDM' 11). ACM Press, Vancouver, Canada, 1242--1247. Google ScholarDigital Library
- Weilong Yao, Jing He, Guangyan Huang, Jie Cao, and Yanchun Zhang. 2015. A graph-based Model for Context-aware Recommendation Using Implicit Feedback Data. World Wide Web 18, 5 (2015), 1351--1371. Google ScholarDigital Library
- Weilong Yao, Jing He, Guangyan Huang, Jie Cao, and Yanchun Zhang. 2015. A Graph-based Model for Context-Aware Recommendation Using Implicit Feedback Data. World Wide Web 18, 5 (2015), 1351--1371. Google ScholarDigital Library
- Yong Zheng, Robin Burke, and Bamshad Mobasher. 2013. Recommendation with Differential Context Weighting. In Proceedings of the 21st International Conference on User Modeling, Adaptation, and Personalization. Springer, Berlin, Heidelberg, 152--164.Google ScholarCross Ref
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2015. Similarity-based Context-aware Recommendation. In Proceedings of the 16th International Conference on Web Information Systems Engineering (WISE' 16). Springer, Miami, USA, 431--447. Google ScholarDigital Library
Index Terms
- Trust and Context-based Rating Prediction using Collaborative Filtering: A Hybrid Approach
Recommendations
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsUser-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Context-Based Rating Prediction using Collaborative Filtering and Linked Open Data
WIMS2019: Proceedings of the 9th International Conference on Web Intelligence, Mining and SemanticsLinked Open Data (LOD) consists of various knowledgebases, such as DBpedia, Yago, and Freebase, and uses structured data as features to conceptualize a domain of interest. During last few years, many researchers have shown how LOD can be utilized in ...
Sequence-based trust in collaborative filtering for document recommendation
Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have ...
Comments