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also realize that the video features provided by our dataset are insufficient for our task, and thus motivate using graph information. We present a node2vec based method for link prediction, and demonstrate its effectiveness by con- ducting experiments where it performs to a high degree of accuracy. We then extend our method for single video recommendation, to a highly scalable Locality Sensitive Hashing based approach. We demonstrate that our fast, approximate method gives very high accuracies on the task of single- video recommendation. We also provide qualitative analysis and examples indicating that our method extends to making predictions from a set of rated videos as well. Future work would include further using graph information and the video features together, to build a more accurate recommender system. It would also be interested to construct a dataset con- taining popular YouTube playlists and how videos get added to them. This would help us gain insight into our problem and provide a test dataset where we may quantitatively evaluate our ap- proach as well. Finally, it would be interesting to run our experiments on the entire YouTube dataset, to measure the robustness and scalability of our approach on such massive amounts of data. References [ASA+10] [BA99] [Bot10] [CAS16] [CDL08] [DLL+10] [Dom07] [GL16] [LNK03] [MCCD13] [RY17] [SMK+01] Dhoha Almazro, Ghadeer Shahatah, Lamia Albdulkarim, Mona Kherees, Romy Mar- tinez, and William Nzoukou. A survey paper on recommender systems. CoRR, abs/1006.5278, 2010. Albert-László Barabási and Réka Albert. Emergence of scaling in random networks. science, 286(5439):509–512, 1999. Léon Bottou. Large-scale machine learning with stochastic gradient descent. In Pro- ceedings of COMPSTAT’2010, pages 177–186. Springer, 2010. Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recommendations. InProceedingsofthe10thACMConferenceonRecommender Systems, New York, NY, USA, 2016. Xu Cheng, Cameron Dale, and Jiangchuan Liu. Statistics and social network of youtube videos, 07 2008. James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. The youtube video recommendation system. In Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10, pages 293–296, New York, NY, USA, 2010. ACM. Ramesh Dommeti. Neighborhood based methods for collaborative filtering, 2007. Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. CoRR, abs/1607.00653, 2016. David Liben-Nowell and Jon Kleinberg. The link prediction problem for social net- works. InProceedingsoftheTwelfthInternationalConferenceonInformationand Knowledge Management, CIKM ’03, pages 556–559, New York, NY, USA, 2003. ACM. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013. Xin Li Rex Ying, Yuanfang Li. Graphnet: Recommendation system based on lan- guage and network structure, 2017. Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, and Hari Balakrishnan. Chord: A scalable peer-to-peer lookup service for internet applications. SIGCOMM Comput. Commun. Rev., 31(4):149–160, August 2001. 10PDF Image | Recommending Related YouTube Videos
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