Content Recommendation and Performance of Caching Systems
Abstract
Recommenders and cache systems affect millions of Internet users nowadays. On the one hand, an important share of the content demand from systems such as Netflix comes from recommendations generated by the Netflix platform itself. On the other hand, cache systems serve a significant portion of this content demand. Although content recommender and cache systems are two of the main components of the current Internet, the relation between them is not yet fully understood. In this paper, we propose models and optimization problems that aim at better understanding this relation. We show that some instances of the formulated problem have a simple closed-form solution. We also present preliminary results using Movielens traces. Our work is a step forward towards a better understanding of the mutual implications of recommender systems and caching algorithms, what we believe to be a promising front to further explore for benefiting both content providers and content consumers in the long run.
References
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Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., e Riedl, J. (2003). Movielens unplugged: experiences with an occasionally connected recommender system. In Proc. of the 8th international conference on Intelligent user interfaces, pages 263–266. ACM.
Munaro, D., Delgado, C., e Menasch´e, D. S. (2015). Content recommendation and service costs in swarming systems. In ICC, pages 5878–5883. IEEE.
Nygren, E., Sitaraman, R. K., e Sun, J. (2010). The akamai network: a platform for high-performance internet applications. Operating Systems Review, 44(3):2–19.
Sermpezis, P., Spyropoulos, T., Vigneri, L., e Giannakas, T. (2017). Femto-caching with soft cache hits: Improving performance through recommendation and delivery of related content. arXiv preprint arXiv:1702.04943.
Turnbull, D. (2017). High-quality recommendation systems with elastic search. [link].
Verhoeyen, M., Vriendt, J. D., e Vleeschauwer, D. D. (2012). Optimizing for video storage networking with recommender systems. Bell Labs Tech., 16(4):97–113.
Amazon, E. (2015). Amazon web services. Available in: [link] (November 2012).
Boyd, S. e Vandenberghe, L. (2004). Convex optimization. Cambridge university press.
Chatzieleftheriou, L. E., Karaliopoulos, M., e Koutsopo, I. (2017). Caching-aware recommendations: Nudging user preferences towards better caching performance. In INFOCOM.
Che, H., Tung, Y., e Wang, Z. (2009). Hierarchical web caching systems: Modeling, design and experimental results. In JSAC.
Dehghan, M., Massoulie, L., Towsley, D., Menasche, D., e Tay, Y. (2016). A utility optimization approach to network cache design. In INFOCOM.
Dernbach, S., Taft, N., Kurose, J., Weinsberg, U., Diot, C., e Ashkan, A. (2016). Cache content-selection policies for streaming video services. In INFOCOM, pages 1–9.
Domingues, G., e Silva, E. d. S., Leão, R. M., Menasché, D. S., e Towsley, D. (2017). Enabling opportunistic search and placement in cache networks. Computer Networks, 119:17–34.
Domingues, G., Leao, R. M., Menasche, D. S., et al. (2015). Flexible content placement in cache networks using reinforced counters. arXiv preprint arXiv:1501.03446.
Fagin, R. (1977). Asymptotic miss ratios over independent references. Journal of Computer and System Sciences, 14(2):222–250.
Fofack, N. C., Dehghan, M., Towsley, D., Badov, M., e Goeckel, D. L. (2014). On the performance of general cache networks. In VALUETOOLS, pages 106–113.
Gill, P., Arlitt, M., Li, Z., e Mahanti, A. (2007). Youtube traffic characterization: a view from the edge. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pages 15–28. ACM.
Gomez-Uribe, C. A. e Hunt, N. (2016). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4):13.
Kaafar, M. A., Berkovsky, S., e Donnet, B. (2013). On the potential of recommendation technologies for efficient content delivery networks. ACM SIGCOMM Computer Communication Review, 43(3):74–77.
Krishnappa, D. K., Zink, M., Griwodz, C., e Halvorsen, P. (2015). Cache-centric video recommendation: an approach to improve the efficiency of youtube caches. ACM Trans. on Multimedia Computing, Communications, and Applications (TOMM), 11(4):48.
Leconte, M., Paschos, G., Gkatzikis, L., Draief, M., Vassilaras, S., e Chouvardas, S. (2016). Placing dynamic content in caches with small population. In INFOCOM, pages 1–9.
Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., e Riedl, J. (2003). Movielens unplugged: experiences with an occasionally connected recommender system. In Proc. of the 8th international conference on Intelligent user interfaces, pages 263–266. ACM.
Munaro, D., Delgado, C., e Menasch´e, D. S. (2015). Content recommendation and service costs in swarming systems. In ICC, pages 5878–5883. IEEE.
Nygren, E., Sitaraman, R. K., e Sun, J. (2010). The akamai network: a platform for high-performance internet applications. Operating Systems Review, 44(3):2–19.
Sermpezis, P., Spyropoulos, T., Vigneri, L., e Giannakas, T. (2017). Femto-caching with soft cache hits: Improving performance through recommendation and delivery of related content. arXiv preprint arXiv:1702.04943.
Turnbull, D. (2017). High-quality recommendation systems with elastic search. [link].
Verhoeyen, M., Vriendt, J. D., e Vleeschauwer, D. D. (2012). Optimizing for video storage networking with recommender systems. Bell Labs Tech., 16(4):97–113.
Published
2017-07-02
How to Cite
DE FREITAS, Raul G. M.; MENASCHÉ, Daniel; DELGADO, Carla; ZIVIANI, Artur.
Content Recommendation and Performance of Caching Systems. In: WORKSHOP ON PERFORMANCE OF COMPUTER AND COMMUNICATION SYSTEMS (WPERFORMANCE), 16. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1684-1697.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2017.3359.
