Content recommendation and service costs in swarming systems

Recommendation systems and the performance of computer network systems have fundamental implications over each other. While recommendation systems impact system performance, the latter can be used to guide the former. In this paper, we study the interconnections between recommendation systems and the performance of the network. Focusing on swarming systems à la Bittorrent, we propose an analytical model to capture the revenue and the cost to a content provider as a function of the quality of its recommendations and the cost to serve the content. The model is then used to suggest heuristics on how to recommend content accounting for service costs and user preferences.

[1]  Rui Santos Cruz,et al.  A P2P streaming architecture supporting scalable media , 2015, Peer-to-Peer Netw. Appl..

[2]  Supawadee Hiranpongsin,et al.  Integration of recommender system for Web cache management , 2013 .

[3]  Carsten Griwodz,et al.  What should you cache?: a global analysis on YouTube related video caching , 2013, NOSSDAV '13.

[4]  Jin Zhao,et al.  GMaker: A video recommendation module for peer-assisted VoD , 2014, Peer Peer Netw. Appl..

[5]  Hermann Hellwagner,et al.  Piece selection algorithms for layered video streaming in P2P networks , 2014, Discret. Appl. Math..

[6]  Rüdiger Schollmeier,et al.  A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications , 2001, Proceedings First International Conference on Peer-to-Peer Computing.

[7]  Tin Yu Wu,et al.  Incentive mechanism for P2P file sharing based on social network and game theory , 2014, J. Netw. Comput. Appl..

[8]  Vijay Erramilli,et al.  Social-Aware Replication in Geo-Diverse Online Systems , 2015, IEEE Transactions on Parallel and Distributed Systems.

[9]  Jim Kurose,et al.  Computer Networking: A Top-Down Approach , 1999 .

[10]  Donald F. Towsley,et al.  Strategic reasoning about bundling in swarming systems , 2009, 2009 International Conference on Game Theory for Networks.

[11]  Donald F. Towsley,et al.  Reciprocity and Barter in Peer-to-Peer Systems , 2010, 2010 Proceedings IEEE INFOCOM.

[12]  Dawei Wang,et al.  Characterizing Application Behaviors for classifying P2P traffic , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[13]  Yang Li,et al.  QoE space based QoE adaptation algorithm for SVC-P2P video streaming systems , 2014, 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC).

[14]  Burkhard Stiller,et al.  Radiommender: P2P on-line radio with a distributed recommender system , 2012, 2012 IEEE 12th International Conference on Peer-to-Peer Computing (P2P).

[15]  Carsten Griwodz,et al.  Cache-Centric Video Recommendation , 2015, ACM Trans. Multim. Comput. Commun. Appl..

[16]  Kibeom Lee,et al.  Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items , 2015, Expert Syst. Appl..

[17]  Laurence T. Yang,et al.  Cloud-Based Mobile Multimedia Recommendation System With User Behavior Information , 2014, IEEE Systems Journal.

[18]  Daniel Sadoc Menasché,et al.  Content Recommendation and Service Cost in P2P Systems , 2014, 2014 Brazilian Symposium on Computer Networks and Distributed Systems.

[19]  Honggang Zhang,et al.  Can Online Social Friends Help to Improve Data Swarming Performance? , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[20]  Honggang Zhang,et al.  Social interaction based video recommendation: Recommending YouTube videos to facebook users , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[21]  P Sreelakshmi,et al.  Leveraging Social Networks for P2P Content-Based File Sharing in Disconnected MANETs , 2015 .

[22]  Jamal Munshi,et al.  A Method for Constructing Likert Scales , 2014 .

[23]  Alexander Tuzhilin,et al.  Towards the Next Generation of Recommender Systems , 2010, ICE-B 2010.

[24]  Amir Nakib,et al.  A learning-based resource allocation approach for P2P streaming systems , 2015, IEEE Network.

[25]  Junjie Yao,et al.  Challenging the Long Tail Recommendation , 2012, Proc. VLDB Endow..