Abstract
There are many recommendation engines present that work on providing users with recommendations based on a single media. This is our attempt to create a recommendation system that works on relationships between different media elements and provides users with the best new media item to choose from. “Hybrid context-based recommendation for media” works on pre-context filtering and a hybrid approach of content and collaborative-based filtering and given a media element as input and recommends the same or different media element that the user needs or demands. The given model has been prepared using the MovieLens 100 K Data set and Goodreads Book Data set. This model can be further enhanced through a systematic consideration of demographic attributes in the future.
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
Similar content being viewed by others
References
Shriver D (2018) Toward the development of richer properties for recommender systems. In: Proceedings of the 40th international conference on software engineering: companion proceedings (ICSE ‘18). Association for Computing Machinery, New York, NY, USA, pp 173–174.
Ricci F, Rokach L, Shapira B (2011) Recommender systems handbook. Springer, pp 1–35
Mohanty S, Chatterjee J, Jain S, Elngar A, Gupta P (2020) Recommender system with machine learning and artificial intelligence. Wiley-Scrivener
Sharma M, Ahuja L, Kumar V (2020) Study and classification of recommender Systems and their techniques: a Survey. In: Gupta D, Khanna A, Bhattacharyya S, Hassanien AE, Anand S, Jaiswal A (eds) International conference on innovative computing and communication. Advances in intelligent systems and computing, vol 1. Springer, Singapore
Paul D, Kundu D (2020) A survey of music recommendation systems with a proposed music recommendation system. In: Emerging technology in modelling and graphics. Springer, Singapore, pp 279–285
Darekar R, Dayma K, Parabh R, Kurhade S (2018) A hybrid model for book recommendation. In: Proceedings of the International Conference on Inventive Communication and Computational Technologies (ICICCT 2018), no. Icicct, pp 120–124
Sharma M, Ahuja L, Kumar V (2019) A hybrid context aware recommender system with combined pre and post-filter approach. Int J Inf Technol Project Manage 10(4):1–14
Soyusiawaty D, Zakaria Y (2018) Book data content similarity detector with cosine similarity. In: 12th international conference on telecommunication systems, services, and applications (TSSA)
Juntui S, Khoenkaw P (2018) Automatic non-personalized book recommender algorithm for bookstore shelf management. In: 2018 international conference on digital arts, media and technology (ICDAMT), pp 49–53
Hariadi I, Nurjanah D (2017)Hybrid attribute and personality based recommender system for book recommendation. In: 2017 international conference on data and software engineering (ICoDSE), pp 1–5
Waga K, Tabarcea A, Fränti P (2011) Context aware recommendation of location-based data. In: 15th international conference on system theory, control and computing, pp 1–6
Renjith S, Sreekumar A, Jathavedan M (2020) An extensive study on the evolution of context-aware personalized travel recommender systems. Inf Process Manage 57(1):102078
. Alhamid MF, Rawashdeh M, Saddik AE (2013)Towards context-aware recommendations of multimedia in an ambient intelligence environment. In: 2013 IEEE International Symposium on Multimedia, pp 409–414
Lops P, Jannach D, Musto C, Bogers T, Koolen M (2019) Trends in content-based recommendation—preface to the special issue on recommender systems based on rich item descriptions. User Model User-Adapt Interact 29(2):239–249
Zarzour H, Al-Sharif Z, Al-Ayyoub M, Jararweh Y (2018) A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 2018 9th international conference on information and communication systems (ICICS), pp 102–106
Lahabar S, Narayanan P (2009) Singular value decomposition on GPU using CUDA. In: Parallel distributed processing, IPDPS 2009. IEEE international symposium, pp 1–10, May 2009
Ji L, Lin G, Tan H (2018) Neural collaborative filtering: hybrid recommendation algorithm with content information and implicit feedback. In: Yin H, Camacho D, Novais P, Tallón-Ballesteros A (eds) Intelligent data engineering and automated learning—IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science, vol 11314. Springer, Cham
Vall A, Dorfer M, Zadeh HE, Schedl M, Burjojee K, Widmer G (2019) Feature-Combination hybrid recommender systems for automated music playlist continuation. User Model User-Adap Inter 29:527–572
Hawashin B, Lafi M, Kanan T, Mansour A (2020) An efficient hybrid similarity measure based on user interests for recommender systems. Expert Syst 37(5):e12471
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajpoot, A.K., Prasad, A.K., Tiwari, G., Rawat, M., Sharma, M. (2022). Hybrid Context-Based Recommendation for Media. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_10
Download citation
DOI: https://doi.org/10.1007/978-981-16-6289-8_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6288-1
Online ISBN: 978-981-16-6289-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)