@article {23628,
	title = {Cache-Centric Video Recommendation: An Approach to Improve the Efficiency of YouTube Caches},
	journal = {ACM Trans. Multimedia Comput. Commun. Appl.},
	volume = {11},
	year = {2015},
	month = {06/2015},
	pages = {1--20},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {In this article, we take advantage of the user behavior of requesting videos from the top of the related list provided by YouTube to improve the performance of YouTube caches. We recommend that local caches reorder the related lists associated with YouTube videos, presenting the cached content above noncached content. We argue that the likelihood that viewers select content from the top of the related list is higher than selection from the bottom, and pushing contents already in the cache to the top of the related list would increase the likelihood of choosing cached content. To verify that the position on the list really is the selection criterion more dominant than the content itself, we conduct a user study with 40 YouTube-using volunteers who were presented with random related lists in their everyday YouTube use. After confirming our assumption, we analyze the benefits of our approach by an investigation that is based on two traces collected from a university campus. Our analysis shows that the proposed reordering approach for related lists would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. This increase in hit rate would lead to reduction in server load and backend bandwidth usage, which in turn reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube{\textquoteright}s content distribution system. An analysis of YouTube{\textquoteright}s recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.},
	keywords = {Caching, recommendation, YouTube},
	issn = {1551-6857},
	doi = {10.1145/2716310},
	url = {http://doi.acm.org/10.1145/2716310},
	author = {Dilip Kumar {Krishnappa} and Zink, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {Simula.simula.2052,
	title = {Cache-Centric Video Recommendation: an Approach to Improve the Efficiency of YouTube Caches},
	journal = {Proceedings of the International Conference on Multimedia Systems (MMSys)},
	year = {2013},
	month = {March},
	pages = {261-270},
	publisher = {ACM},
	type = {Conference},
	abstract = {In this paper, we take advantage of the user behavior of re- questing videos from the related list provided by YouTube and the user behavior of requesting videos from the top of this related list to improve the performance of YouTube{\textquoteright}s caches. We recommend a related list reordering approach which modifies the order of the videos shown on the related list based on the content in the cache. The main goal of our reordering approach is to push the contents already in the cache to the top of the related list and push non-cached contents towards the bottom, which increases the likelihood that the already cached content will be chosen by the viewer. We analyze the benefits of our approach by an investigation that is based on two traces collected from an university campus. Our analysis shows that the proposed reordering approach for related list would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. The increase in hit rate would lead to a 5.12\% to 18.19\% reduction in server load or back-end bandwidth usage. This increase in hit rate and reduction in back-end bandwidth reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube{\textquoteright}s content distribution system. An analysis of YouTube{\textquoteright}s recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.},
	keywords = {Conference},
	author = {Dilip Kumar {Krishnappa} and Zink, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l},
	editor = {Griwodz, Carsten}
}
@inproceedings {Simula.simula.2610,
	title = {What Should You Cache?: a Global Analysis on YouTube Related Video Caching},
	journal = {Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video},
	year = {2013},
	month = {March},
	pages = {31-36},
	publisher = {ACM},
	type = {Annual},
	address = {unknown},
	abstract = {Following advice from the YouTube recommendation system is one of the ways users browse through the videos offered by YouTube. The system presents related videos based on several factors depending on the current video requested. This related videos list can be used by caching infrastructure to reduce network bandwidth consumption. In this paper, we analyze the differences between user-specific recommendation lists. We perform this analysis on 100s of user nodes from all around the world divided into 4 geographical regions using PlanetLab. Based on our analysis, we find that the related videos differ less in the top half (1-10) of the related video list offered by YouTube compared to the bottom half (11-20). Based on our analysis, we suggest that, caching or prefetching of the Top 10 of the related videos is advantageous over a period of time than caching the whole list offered by YouTube.},
	doi = {10.1145/2460782.2460788},
	author = {Dilip Kumar {Krishnappa} and Zink, Michael and Griwodz, Carsten}
}
