https://oldena.lpnu.ua/handle/ntb/56851| Title: | Online Video Platform with Context-Aware Content-Based Recommender System |
| Authors: | Pisotskyi, Marian Botchkaryov, Alexey |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | Pisotskyi M. Online Video Platform with Context-Aware Content-Based Recommender System / Marian Pisotskyi, Alexey Botchkaryov // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 6. — No 1. — P. 46–53. |
| Bibliographic description (International): | Pisotskyi M. Online Video Platform with Context-Aware Content-Based Recommender System / Marian Pisotskyi, Alexey Botchkaryov // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 6. — No 1. — P. 46–53. |
| Is part of: | Advances in Cyber-Physical Systems, 1 (6), 2021 |
| Issue: | 1 |
| Issue Date: | 1-Mar-2021 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів Lviv |
| DOI: | https://doi.org/10.23939/acps2021.01.046 |
| Keywords: | online video platform context awareness content-based recommender system |
| Number of pages: | 8 |
| Page range: | 46-53 |
| Start page: | 46 |
| End page: | 53 |
| Abstract: | The problem of developing an online video platform with a context-aware content-based recommender system has been considered. Approaches to developing online video platforms have been considered. A comparison of popular online video platforms has been presented. A method of context-aware content-based recommendation of videos has been proposed. A method involves saving information about user interaction with video, obtaining and storing information about which videos the user liked, determining user context, composing a profile of user preferences, composing a profile of user preferences depending on context, determining the similarity between the video profile and a profile of user preferences (with and without context consideration), determining the relevance of the video to the context, the conclusive estimation of the relevance of the video to the user's preferences based on the proposed composite relevance indicator. The developed structure of online video platform has been presented. The algorithm of its work has been considered. The structure of the online video platform database has been proposed. Features of designing the user interface of the online video platform have been considered. The issue of testing the developed online video platform has been considered. |
| URI: | https://ena.lpnu.ua/handle/ntb/56851 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2021 © Pisotskyi M., Botchkaryov A., 2021 |
| References (Ukraine): | [1]Lee, J. (2005). Scalable Continuous Media Streaming Systems: Architecture, Design, Analysis and Implementation. Wiley. 394 p. [2]Ce Zhu, Yuenan Li, Xiamu Niu (2010). Streaming Media Architectures, Techniques, and Applications: Recent Advances. IGI Global. 502 p. [3]Dang Nam Chi Nguyen (2006). Scalable and CostEffectiveFramework for ContinuousMedia-On-Demand, Ph.D. Thesis, University of Technology Sydney. 137 p. [4]Parthasarathy Ranganathan et al. (2021). Warehouse-scale video acceleration: co-design and deployment in the wild. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 600–615. [5]Li, H. and Liu, J. (2012). Video Sharing in Online Social Network: Measurement and Analysis. In: Proceedings of ACM NOSSDAV'12. Toronto, Canada, pp. 83–88. [6]Davidson, J., Liebald, B., Liu, J. and Nandy, P. (2010). The YouTube video recommendation system. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010.Barcelona, Spain, pp. 293–296. [7]Zhe Zhao et al. (2019). Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems (RecSys'19), pp.43–51. [8]Cheuque, G., Guzmán, J. and Parra, D. (2019). Recommender Systems for Online Video Game Platforms: the Case of STEAM. In: Proceedings of The 2019 World Wide Web Conference, pp. 763–771. [9]Ricci, F., Rokach, L., Shapira, B. and Kantor, P. (eds.) (2015). Recommender Systems Handbook. 2nd ed., Springer. 1020 p. [10] Aggarwal, C. (2016). Recommender Systems: The Textbook. Springer. 519 p. [11] Schrage, M. (2020). Recommendation Engines. The MIT Press. 296 p. [12] Falk, K. (2019). Practical Recommender Systems. Manning Publications. 432 p. [13] Robillard, M., Maalej, W., Walker, R. and Zimmermann, T. (eds.) (2014). Recommendation Systems in Software Engineering. Springer-Verlag Berlin Heidelberg. 560 p. [14] Jannach, D. (2010). Recommender Systems: An Introduction. Cambridge University Press. 352 p. [15] Jie Lu, Qian Zhang, Guangquan Zhang (2020). Recommender Systems: Advanced Developments. WSPC. 362 p. [16] Suresh Kumar Gorakala (2017). Building Recommendation Engines. Packt Publishing. 357 p. [17] Neumann, A. (2009). Recommender Systems for Information Providers: Designing Customer Centric Paths to Information. Physica-Verlag Heidelberg. 158 p. [18] Isinkayea, F., Folajimib, Y. and Ojokohc, B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, Volume 16, Issue 3, November, pp. 261–273. [19] Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang and Guangquan Zhang (2015). Recommender system application developments: A survey. Decision Support Systems, Vol. 74, pp. 12–-32. [20] Leskovec, J., Rajaraman, A., Ullman, J. (2020). Mining of Massive Datasets. 3rd ed. Cambridge University Press 565 p. [21] Connor, R. (2016). A Tale of Four Metrics. In: Amsaleg, L., Houle, M., Schubert, E. (eds.) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science, vol. 9939. Springer, pp. 210–217. [22] Schilit, B., Adams, N. and Want, R. (1994) Context-aware computing applications. In: Proceedings of the IEEE Workshop on “Mobile Computing Systems and Applications”, IEEE Computer Society, pp. 85–90. [23] Abowd, G., Dey, A., Brown, P., Davies, N., Smith, M. and Steggles, P. (1999) Towards a Better Understanding of Context and Context-Awareness. In: Gellersen, H. (ed.) Handheld and Ubiquitous Computing. Lecture Notes in Computer Science, vol 1707. Springer, Berlin, Heidelberg. pp. 304–307 [24] Bolchini, C., Curino, C., Quintarelli, E., Schreiber, F. and Tanca L. (2007). A data-oriented survey of context models. ACM SIGMOD Record, 36, 4, pp. 19–26 [25] Perera, C., Zaslavsky, A., Christen, P. and Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, vol. 16, no. 1, First Quarter, pp. 414–454. [26] Grifoni, P., D’Ulizia, A., and Ferri, F. (2018). Context-Awareness in Location Based Services in the Big Data Era, In: Skourletopoulos, G., Mastorakis, G., Mavromoustakis, C., Dobre C. and Pallis, E. (eds.) Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, Springer, vol. 10, pp. 85–127. [27] Capurso, N., Bo Mei, Tianyi Song and Xiuzhen Cheng (2018). A survey on key fields of context awareness for mobile devices. Journal of Network and Computer Applications, Vol. 118, pp. 44–60. [28] Botchkaryov, A. (2018). Context-Aware Task Sequence Planning for Autonomous Intelligent Systems. Advances in Cyber-Physical Systems, Lviv, Vol. 3, Nr 2, pp. 60–66. [29] Adomavicius G. and Tuzhilin A. (2011) Context-Aware Recommender Systems. In: Recommender Systems Handbook, ed. by Francesco Ricci et al., Springer, pp. 217–253. [30] Adomavicius, G., Mobasher, B., Ricci F. and Tuzhilin A. (2011). Context-Aware Recommender Systems. Ai Magazine, 32(3), pp. 67–80. [31] Abbar, S., Bouzeghoub, M., Lopez, S. (2009). Context-Aware Recommender Systems: A Service-Oriented Approach. In: Proceedings of the 3rd International Workshop on Personalized Access, Profile Management and Context Awareness in Databases (PersDB). Lyon, France. [32] Shaina Raza and Chen Ding (2019) Progress in context-aware recommender systems: An overview. Computer Science Review, Vol. 31, pp. 84–97. [33] Nawrocki, P., Śnieżyński, B. and Czyżewski, J. (2016). Learning Agent for a Service-Oriented Context-Aware Recommender System in a Heterogeneous Environment, Computing and Informatics, Vol. 35, pp. 1005–1026. [34] Bouneffouf, D. (2012). Following the User's Interests in Mobile Context-Aware Recommender Systems: The Hybrid-e-greedy Algorithm. In: Proceedings of the 2012 26th International Conference on Advanced Information Networking and Applications Workshops, Lecture Notes in Computer Science, IEEE Computer Society, pp. 657–662. |
| References (International): | [1]Lee, J. (2005). Scalable Continuous Media Streaming Systems: Architecture, Design, Analysis and Implementation. Wiley. 394 p. [2]Ce Zhu, Yuenan Li, Xiamu Niu (2010). Streaming Media Architectures, Techniques, and Applications: Recent Advances. IGI Global. 502 p. [3]Dang Nam Chi Nguyen (2006). Scalable and CostEffectiveFramework for ContinuousMedia-On-Demand, Ph.D. Thesis, University of Technology Sydney. 137 p. [4]Parthasarathy Ranganathan et al. (2021). Warehouse-scale video acceleration: co-design and deployment in the wild. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 600–615. [5]Li, H. and Liu, J. (2012). Video Sharing in Online Social Network: Measurement and Analysis. In: Proceedings of ACM NOSSDAV'12. Toronto, Canada, pp. 83–88. [6]Davidson, J., Liebald, B., Liu, J. and Nandy, P. (2010). The YouTube video recommendation system. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010.Barcelona, Spain, pp. 293–296. [7]Zhe Zhao et al. (2019). Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems (RecSys'19), pp.43–51. [8]Cheuque, G., Guzmán, J. and Parra, D. (2019). Recommender Systems for Online Video Game Platforms: the Case of STEAM. In: Proceedings of The 2019 World Wide Web Conference, pp. 763–771. [9]Ricci, F., Rokach, L., Shapira, B. and Kantor, P. (eds.) (2015). Recommender Systems Handbook. 2nd ed., Springer. 1020 p. [10] Aggarwal, C. (2016). Recommender Systems: The Textbook. Springer. 519 p. [11] Schrage, M. (2020). Recommendation Engines. The MIT Press. 296 p. [12] Falk, K. (2019). Practical Recommender Systems. Manning Publications. 432 p. [13] Robillard, M., Maalej, W., Walker, R. and Zimmermann, T. (eds.) (2014). Recommendation Systems in Software Engineering. Springer-Verlag Berlin Heidelberg. 560 p. [14] Jannach, D. (2010). Recommender Systems: An Introduction. Cambridge University Press. 352 p. [15] Jie Lu, Qian Zhang, Guangquan Zhang (2020). Recommender Systems: Advanced Developments. WSPC. 362 p. [16] Suresh Kumar Gorakala (2017). Building Recommendation Engines. Packt Publishing. 357 p. [17] Neumann, A. (2009). Recommender Systems for Information Providers: Designing Customer Centric Paths to Information. Physica-Verlag Heidelberg. 158 p. [18] Isinkayea, F., Folajimib, Y. and Ojokohc, B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, Volume 16, Issue 3, November, pp. 261–273. [19] Jie Lu, Dianshuang Wu, Mingsong Mao, Wei Wang and Guangquan Zhang (2015). Recommender system application developments: A survey. Decision Support Systems, Vol. 74, pp. 12–-32. [20] Leskovec, J., Rajaraman, A., Ullman, J. (2020). Mining of Massive Datasets. 3rd ed. Cambridge University Press 565 p. [21] Connor, R. (2016). A Tale of Four Metrics. In: Amsaleg, L., Houle, M., Schubert, E. (eds.) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science, vol. 9939. Springer, pp. 210–217. [22] Schilit, B., Adams, N. and Want, R. (1994) Context-aware computing applications. In: Proceedings of the IEEE Workshop on "Mobile Computing Systems and Applications", IEEE Computer Society, pp. 85–90. [23] Abowd, G., Dey, A., Brown, P., Davies, N., Smith, M. and Steggles, P. (1999) Towards a Better Understanding of Context and Context-Awareness. In: Gellersen, H. (ed.) Handheld and Ubiquitous Computing. Lecture Notes in Computer Science, vol 1707. Springer, Berlin, Heidelberg. pp. 304–307 [24] Bolchini, C., Curino, C., Quintarelli, E., Schreiber, F. and Tanca L. (2007). A data-oriented survey of context models. ACM SIGMOD Record, 36, 4, pp. 19–26 [25] Perera, C., Zaslavsky, A., Christen, P. and Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, vol. 16, no. 1, First Quarter, pp. 414–454. [26] Grifoni, P., D’Ulizia, A., and Ferri, F. (2018). Context-Awareness in Location Based Services in the Big Data Era, In: Skourletopoulos, G., Mastorakis, G., Mavromoustakis, C., Dobre C. and Pallis, E. (eds.) Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, Springer, vol. 10, pp. 85–127. [27] Capurso, N., Bo Mei, Tianyi Song and Xiuzhen Cheng (2018). A survey on key fields of context awareness for mobile devices. Journal of Network and Computer Applications, Vol. 118, pp. 44–60. [28] Botchkaryov, A. (2018). Context-Aware Task Sequence Planning for Autonomous Intelligent Systems. Advances in Cyber-Physical Systems, Lviv, Vol. 3, Nr 2, pp. 60–66. [29] Adomavicius G. and Tuzhilin A. (2011) Context-Aware Recommender Systems. In: Recommender Systems Handbook, ed. by Francesco Ricci et al., Springer, pp. 217–253. [30] Adomavicius, G., Mobasher, B., Ricci F. and Tuzhilin A. (2011). Context-Aware Recommender Systems. Ai Magazine, 32(3), pp. 67–80. [31] Abbar, S., Bouzeghoub, M., Lopez, S. (2009). Context-Aware Recommender Systems: A Service-Oriented Approach. In: Proceedings of the 3rd International Workshop on Personalized Access, Profile Management and Context Awareness in Databases (PersDB). Lyon, France. [32] Shaina Raza and Chen Ding (2019) Progress in context-aware recommender systems: An overview. Computer Science Review, Vol. 31, pp. 84–97. [33] Nawrocki, P., Śnieżyński, B. and Czyżewski, J. (2016). Learning Agent for a Service-Oriented Context-Aware Recommender System in a Heterogeneous Environment, Computing and Informatics, Vol. 35, pp. 1005–1026. [34] Bouneffouf, D. (2012). Following the User's Interests in Mobile Context-Aware Recommender Systems: The Hybrid-e-greedy Algorithm. In: Proceedings of the 2012 26th International Conference on Advanced Information Networking and Applications Workshops, Lecture Notes in Computer Science, IEEE Computer Society, pp. 657–662. |
| Content type: | Article |
| Appears in Collections: | Advances In Cyber-Physical Systems. – 2021. – Vol. 6, No. 1 |
| File | Description | Size | Format | |
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| 2021v6n1_Pisotskyi_M-Online_Video_Platform_with_46-53.pdf | 299.99 kB | Adobe PDF | View/Open | |
| 2021v6n1_Pisotskyi_M-Online_Video_Platform_with_46-53__COVER.png | 557.38 kB | image/png | View/Open |
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