Abstract
In the digital world, it has become a challenging task to find items that suit users’ persona and fulfill their need. The reason behind this problem is the unprecedented growth of content and product available online. Recommender System (RS) has emerged as a tool which provides personalized results to users as well as suggestion based on its behavior and past history. Collaborative Filtering (CF), the widely used technique, in the field of RS, provides useful recommendations to users based on similar users. Traditional recommendation approaches such as collaborative filtering and content-based filtering, work on two dimensions, i.e., user-item pair. In addition to this “Context used as third dimension”, is also getting popular among researchers. In the present paper, a new method is proposed, i.e., Context-Aware Recommender System by utilizing both item as well as user preferences based on splitting criteria for movie recommendation applications. In this method, first single item is split into two virtual items based on contextual value and a modified dataset is created. Then, the single user is split into two virtual users based on contextual values. Splitting of any user or item is done only if there is a significant difference between two virtual items (users). Further user-based collaborative filtering is used to generate effective recommendations. The results show the effectiveness of proposed scheme in terms of various performance measure criteria using LDOSCOMODA dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-based Systems, 46, 109–132.
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.
Anderson, C., & Hiralall, M. (2009). Recommender systems for e-shops. Business Mathematics and Informatics paper.
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp. 73–105). US: Springer.
Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 4.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (pp. 285–295). ACM.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994, October). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (pp. 175–186). ACM.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370.
Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender Systems Handbook (pp. 1–35). US: Springer.
Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4–7.
Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems. In Recommender Systems Handbook (pp. 217–253). US: Springer.
Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23(1), 103–145.
Panniello, U., Tuzhilin, A., & Gorgoglione, M. (2014). Comparing context-aware recommender systems in terms of accuracy and diversity. User Modeling and User-Adapted Interaction, 24(1–2), 35–65.
Adomavicius, G., & Tuzhilin, A. (2001). Extending recommender systems: A multidimensional approach. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-01), Workshop on Intelligent Techniques for Web Personalization (ITWP2001) (pp. 4–6). Seattle, Washington: Citeseer.
Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., et al. (2012). Context-aware recommender systems for learning: a survey and future challenges. IEEE Transactions on Learning Technologies, 5(4), 318–335.
Anand, S. S., & Mobasher, B. (2006, September). Contextual recommendation. In Workshop on Web Mining (pp. 142–160). Berlin: Springer.
Baltrunas, L., &Ricci, F. (2014). Experimental evaluation of context-dependent collaborative filtering using item splitting. User Modeling and User-Adapted Interaction, 24(1–2), 7–34.
Wu, H., Liu, X., Pei, Y., & Li, B. (2014, October). Enhancing context-aware recommendation via a unified graph model. In International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI), 2014 (pp. 76–79). IEEE.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5–53.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, M., Sahu, H., Sharma, N. (2019). A Personalized Context-Aware Recommender System Based on User-Item Preferences. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-1274-8_28
Download citation
DOI: https://doi.org/10.1007/978-981-13-1274-8_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1273-1
Online ISBN: 978-981-13-1274-8
eBook Packages: EngineeringEngineering (R0)