A Recommendation System Based on Implicit Data for Internet Protocol Television (IPTV)
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Abstract
IPTV delivers television content over Internet Protocol (IP) networks. Videos On Demand (VOD) is the most popular IPTV, allowing users to freely select from a vast pool of program genres. Therefore, it is necessary to introduce innovative features to attract new users and retain existing ones. For this purpose, IPTV systems typically use VOD recommendation engines. The primary purpose of recommendation systems is to suggest user-relevant items from various items by producing a list of recommendations for each user. In this paper, we introduce an approach to recommendation systems in IPTV. We developed this approach on implicit feedback derived from users’ interaction with movies/series sets, such as how many times they watched a movie and how long they have spent watching specific movies/series. For the previous factors, we tested a variety of recommendation algorithms, content-based, collaborative-based, and hybrid. Then applied the previously mentioned algorithms on real-life big data sets after introducing some modifications to the algorithms, then benchmarked the results on multiple performance metrics. We noticed that the applied changes achieved promising results.
Article Details
How to Cite
[1]
lama mansour, Z. Omran, G. M. Kaddoura, M. Dakkak, and Y. Rahhal, “A Recommendation System Based on Implicit Data for Internet Protocol Television (IPTV)”, AJSE, vol. 22, no. 2, pp. 145 - 152, Aug. 2023.
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References
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[28] M. Mendoza and N. Torres, “Evaluating content novelty in recommender systems,” Journal of Intelligent Information Systems, vol. 54, no. 2, pp. 297–316, Apr. 2020, doi: 10.1007/s10844-019- 00548-x.
[2] G. Jawaheer, M. Szomszor, and P. Kostkova, “Comparison of Implicit and Explicit Feedback from an Online Music Recommendation Service,” in Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, 2010, pp. 47–51. doi: 10.1145/1869446.1869453.
[3] Peter. Brusilovsky, Association for Computing Machinery., and S. ACM Conference on Recommender Systems (4th : 2010 : Barcelona, Content-based movie recommendation system using genre correlation. In Smart Intelligent Computing and Applications. Association for Computing Machinery, 2010.
[4] R. H. Singh, S. Maurya, T. Tripathi, T. Narula, and
G. Srivastav, “Movie Recommendation System using Cosine Similarity and KNN,” International Journal of Engineering and Advanced Technology (IJEAT), no. 9, pp. 2249–8958, 2020, doi: 10.35940/ijeat.E9666.069520.
[5] S. Biswas, L. V. S. Lakshmanan, and S. B. Roy, “Combating the Cold Start User Problem in Model Based Collaborative Filtering,” ArXiv, vol. abs/1703.00397, 2017.
[6] N. F. AL-Bakri and S. H. Hashim, “Collaborative Filtering Recommendation Model Based on k-means Clustering,” Al-Nahrain Journal of Science, vol. 22, no. 1, pp. 74–79, Mar. 2019, doi: 10.22401/ANJS.22.1.10.
[7] Y. He, C. Wang, and C. Jiang, “Correlated Matrix Factorization for Recommendation with Implicit Feedback,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 3, pp. 451–464, Mar. 2019, doi: 10.1109/TKDE.2018.2840993.
[8] M. Patil, S. Brid, and S. Dhebar, “COMPARISON OF DIFFERENT MUSIC RECOMMENDATION SYSTEM ALGORITHMS”.
[9] M. Fu, H. Qu, Z. Yi, L. Lu, and Y. Liu, “A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System,” IEEE Transactions on Cybernetics, vol. 49, no. 3, pp. 1084–1096, 2019, doi: 10.1109/TCYB.2018.2795041.
[10] S. Rendle, W. Krichene, L. Zhang, and J. Anderson, “Neural Collaborative Filtering vs. Matrix Factorization Revisited,” in RecSys 2020 - 14th ACM Conference on Recommender Systems, Sep. 2020, pp. 240–248. doi: 10.1145/3383313.3412488.
[11] N. Hurley and M. Zhang, “Novelty and Diversity in top-N recommendation-Analysis and evaluation,” ACM Transactions on Internet Technology, vol. 10, no. 4, Mar. 2011, doi: 10.1145/1944339.1944341.
[12] K. Pripužić et al., “Building an IPTV VoD recommender system: An experience report DL- Tags: DLT and Smart Tags for decentralized, privacy-preserving and verifiable supply chain management View project Building an IPTV VoD Recommender System: An Experience Report,” 2013. [Online]. Available: https://www.researchgate.net/publication/25643774 8
[13] X. Yin et al., “Time Context-Aware IPTV Program Recommendation Based on Tensor Learning,” in 2018 IEEE Global Communications Conference (GLOBECOM), 2018, pp. 1–6. doi: 10.1109/GLOCOM.2018.8647211.
[14] M. Uluyagmur, Z. Cataltepe, and E. Tayfur, “Content-based movie recommendation using different feature sets,” in Proceedings of the world congress on engineering and computer science, 2012, vol. 1, pp. 17–24.
[15] M. Ilhami and Suharjito, “Film recommendation systems using matrix factorization and collaborative filtering,” in 2014 International Conference on Information Technology Systems and Innovation, ICITSI 2014 - Proceedings, Feb. 2014, pp. 1–6. doi: 10.1109/ICITSI.2014.7048228.
[16] Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009, doi: 10.1109/MC.2009.263.
[17] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua, “Neural collaborative filtering,” in 26th International World Wide Web Conference, WWW 2017, 2017, pp. 173–182. doi: 10.1145/3038912.3052569.
[18] B. Kupisz and O. Unold, “Collaborative filtering recommendation algorithm based on Hadoop and Spark,” in 2015 IEEE International Conference on Industrial Technology (ICIT), 2015, pp. 1510–1514.
[19] A. Said and A. Bellogín, “Comparative recommender system evaluation: Benchmarking recommendation frameworks,” in RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems, Oct. 2014, pp. 129–136. doi: 10.1145/2645710.2645746.
[20] P. Cremonesi, F. Garzotto, S. Negro, A. V. Papadopoulos, and R. Turrin, “LNCS 6948 - Looking for ‘Good’ Recommendations: A Comparative Evaluation of Recommender Systems,” 2011.
[21] Y. Koren, “The BellKor Solution to the Netflix Grand Prize,” 2009. [Online]. Available: www.netflixprize.com/leaderboard
[22] S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-based movie recommendation system using genre correlation,” in Smart Innovation, Systems and Technologies, 2019, vol. 105, pp. 391–397. doi: 10.1007/978-981-13-1927- 3_42.
[23] S. A. Stein, G. Weiss, Y. Chen, and D. D. Leeds, “A College Major Recommendation System,” Fourteenth ACM Conference on Recommender Systems, 2020.
[24] G. Stamatelatos, G. Drosatos, S. Gyftopoulos, H. Briola, and P. S. Efraimidis, “Point-of-interest lists and their potential in recommendation systems,” Information Technology and Tourism, vol. 23, no. 2, pp. 209–239, Jun. 2021, doi: 10.1007/s40558-021- 00195-5.
[25] O. Jeunen, “Revisiting offline evaluation for implicit-feedback recommender systems,” in RecSys 2019 - 13th ACM Conference on Recommender Systems, Sep. 2019, pp. 596–600. doi: 10.1145/3298689.3347069.
[26] H. Xiao, Y. Chen, and X. Shi, “Multi-Perspective Neural Architecture for Recommendation System,” Jul. 2018, [Online]. Available: http://arxiv.org/abs/1807.09751
[27] Y. Chung, N. R. Kim, C. Y. Park, and J. H. Lee, “Improved neighborhood search for collaborative filtering,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 18, no. 1, pp. 29–40, Mar. 2018, doi: 10.5391/IJFIS.2018.18.1.29.
[28] M. Mendoza and N. Torres, “Evaluating content novelty in recommender systems,” Journal of Intelligent Information Systems, vol. 54, no. 2, pp. 297–316, Apr. 2020, doi: 10.1007/s10844-019- 00548-x.