Abstract
Recommendation systems have a wide range of applications today in the digital world. The recommender system must be able to accurately predict the users’ tastes as well as broaden their horizon about the available products. There are various dimensions in which recommendation systems are created and evaluated. Accuracy and diversity play an important role in the recommendation systems and a trade-off must be identified between the two parameters to suit the business requirements. The proposed system makes use of various recommendation approaches to give a wide range of recommendations to users. The recommendations are provided based on similarity of the selected artist, top artists in a genre, using a hybrid model and artists listened by users’ friends. Some recommendations would include the most popular ones, and some would be randomly picked for a diverse range of recommendations.
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
Bhumichitr, K., Channarukul, S., Saejiem, N., Jiamthapthaksin, R., & Nongpong, K. (2017, July). Recommender Systems for university elective course recommendation. Presented at the 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), Nakhon Si Thammarat, Thailand. https://doi.org/10.1109/jcsse.2017.8025933.
Ekvall, N. (2012). Movie recommendation system based on clustered low-rank approximation. Master’s Thesis, Department of Mathematics, Linkoping University, Sweden.
Shakirova, E. (2017). Collaborative filtering for music recommender system. Presented at the IEEE Conference of Russian, Young Researchers in Electrical and Electronic Engineering (EIConRus), St. Petersburg, Russia. https://doi.org/10.1109/elconrus.2017.7910613.
Javari, A., & Jalili, M. (2014). A probabilistic model to resolve diversity–accuracy challenge of recommendation systems. In: Knowledge and Information Systems, London (pp. 609–627). https://doi.org/10.1007/s10115-014-0779-2.
Ge, M., & Persia, F. (2017). Research challenges in multimedia recommender systems. Presented at the IEEE 11th International Conference on Semantic Computing, San Diego, CA, USA. https://doi.org/10.1109/icsc.2017.31.
Flodman, M. (2015). Building a sporting goods recommendation system. Degree project in Computer Science, Second Level KTH Royal Institute of Technology 26.
Ciurana Simó, E. R. (2012). Development of a Tourism recommender system. Master of Science Thesis, Master in Artificial Intelligence, (UPC-URV-UB).
Wang, X., Luo, F., Qian, Y., & Ranzi, G. (2016). A personalized electronic movie recommendation system based on support vector machine and improved particle swarm optimization. PLoS ONE, 11, e0165868. https://doi.org/10.1371/journal.pone.0165868.
Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 1–19. https://doi.org/10.1155/2009/421425.
Chaturvedi, A. K. (2017). Recommender system for news articles using supervised learning. MISS Master Thesis, Department of Information and Communication Technologies—UPF, Barcelona, Spain.
Gorakala, S. K., & Usuelli, M. (2015). Building a recommendation system with R: Learn the art of building robust and powerful recommendation engines using R. UK: Packt Publishing.
Pan, C., & Li, W. (2010, June). Research paper recommendation with topic analysis. Presented at the International Conference on Computer Design and Applications, Beijing, China. https://doi.org/10.1109/iccda.20105541170.
Zaharchuk, V., Ignatov, D. I., & Konstantinov, A. (2012). A new recommender system for the interactive radio network FMhost. National Research University Higher School of Economic 12.
Le, T. Q., & Pishva, D. (2016, January). An innovative tour recommendation system for tourists in Japan. Presented at the 18th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, South Korea. https://doi.org/10.1109/icact.2015.7224843.
Jiang, D., & Shang, W. (2017). Design and implementation of recommendation system of micro video’s topic. In IEEEACIS 16th International Conference on Computer and Information Science ICIS 3. https://doi.org/10.1109/icis.2017.7960040.
Hahsler, M. (2015). Recommenderlab: A framework for developing and testing recommendation algorithms. CRAN, 40.
Dou, Y., Yang, H., & Deng, X. (2016, August). A survey of collaborative filtering algorithms for social recommender systems. Presented at the 12th International Conference on Semantics, Knowledge and Grids, Beijing, China. https://doi.org/10.1109/skg.2016.014.
Gemmell, J., Schimoler, T., Mobasher, B., & Burke, R. (2012). Resource recommendation in social annotation systems: A linear-weighted hybrid approach. Journal of Computer and System Sciences, 78, 1160–1174. https://doi.org/10.1016/j.jcss.2011.10.006.
Ramesh, B., & Reeba, R. (2017, April). Secure recommendation system for E-commerce website. Presented at the International Conference on circuits Power and Computing Technologies [ICCPCT], Kollam, India. https://doi.org/10.1109/iccpct.2017.8074240.
Paraschakis, D., Nilsson, B. J., & Hollander, J. (2015, December). Comparative evaluation of Top-N recommenders in e-Commerce: An industrial perspective. Presented at the IEEE 14th International Conference on Machine Learning and Applications, Miami, FL, USA. https://doi.org/10.1109/icmla.2015.183.
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
Dhruv, A., Kamath, A., Powar, A., Gaikwad, K. (2019). Artist Recommendation System Using Hybrid Method: A Novel Approach. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_44
Download citation
DOI: https://doi.org/10.1007/978-981-13-5953-8_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5952-1
Online ISBN: 978-981-13-5953-8
eBook Packages: EngineeringEngineering (R0)