Abstract
Recommendation networks, where online objects such as books, songs, movies are connected to similar others via hyperlinks, have been considered of great significance for the system to distribute the web traffic, and for users to explore relevant information. However, where are the recommendation networks leading users to, and how efficient are they navigating users are still open questions. In this paper, we study the topology of, and user behaviours on a book recommendation network collected from Amazon based on a width-first search. A self-avoiding random walk model is applied to describe users’ surfing behaviour on such network. We show that the recommendation network tends to rapidly navigate users to very popular books, leading to the monopoly of traffic by these blockbusters. The niche books are therefore hidden in the dominance of the popular ones. As a consequence, the book recommendation network is found unable to quickly and accurately navigate users to their potential interests, especially these niche ones.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The list may change from time to time and in our collection, the seeds were collected on 1st January 2016. The list is accessible at www.amazon.com/gp/bestsellers/books.
References
Buldú, J.M., Cano, P., Koppenberger, M., Almendral, J.A., Boccaletti, S.: The complex network of musical tastes. New J. Phys. 9(6), 172 (2007)
Cano, P., Celma, O., Koppenberger, M., Buldu, J.M.: Topology of music recommendation networks. Chaos 16(1), 013107 (2006)
Carmi, E., Oestreicher-Singer, G., Sundararajan, A.: Spreading the Oprah effect: the diffusion of demand shocks in a recommendation network. In: 30th International Conference Information System, p. 78 (2009)
Carmi, E., Oestreicher-Singer, G., Stettner, U., Sundararajan, A.: Is Oprah contagious? The depth of diffusion of demand shocks in a product network. MIS Quart. 41(1), 207–221 (2017)
Celma, Ó., Cano, P.: From hits to niches? or how popular artists can bias music recommendation and discovery. In: Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition (ACM), p. 5 (2008)
Goldenberg, J., Oestreicher-Singer, G. Reichman, S.: The quest for content: the integration of product networks and social networks in online content exploration. In: 31st International Conference Information System, p. 90 (2010)
Herrero, C.P.: Self-avoiding walks on scale-free networks. Phys. Rev. E 71(1), 016103 (2005)
Hou, L., Liu, K.: Common neighbour structure and similarity intensity in complex networks. Phys. Lett. A 381(39), 3377–3383 (2017)
Hou, L., Liu, K., Liu, J., Zhang, R.: Solving the stability-accuracy-diversity dilemma of recommender systems. Phys. A 468, 415–424 (2017)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media?. In: Proceedings of the 19th International Conference on WWW (ACM), pp. 591–600 (2010)
Leem, B., Chun, H.: An impact of online recommendation network on demand. Expert Syst. Appl. 41(4), 1723–1729 (2014)
Liu, J.G., Hou, L., Pan, X., Guo, Q., Zhou, T.: Stability of similarity measurements for bipartite networks. Sci. Rep. 6, 18653 (2016)
Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Recommender Systems Handbook, pp. 73–105. Springer, US (2011)
Oestreicher-Singer, G., Libai, B., Sivan, L., Carmi, E., Yassin, O.: The network value of products. J. Mark. 77(3), 1–14 (2013)
Oestreicher-Singer, G., Sundararajan, A.: Recommendation networks and the long tail of electronic commerce. MIS Quart. 36(1), 65–83 (2012)
Oestreicher-Singer, G., Sundararajan, A.: The visible hand? Demand effects of recommendation networks in electronic markets. Manage. Sci. 58(11), 1963–1981 (2012)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word of mouth. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217 (1995)
Thompson, G., Kello, C. T., Montez, P.: Searching semantic memory as a scale-free network: evidence from category recall and a wikipedia model of semantics. Ann. Meet. Cog. Sci. (2013)
Thompson, G., Kello, C.T.: Walking across Wikipedia: a scale-free network model of semantic memory retrieval. Fron. Psychol. 5, 86 (2014)
Travencolo, B.A.N., Costa, L.D.F.: Accessibility in complex networks. Phys. Lett. A 373(1), 89–95 (2008)
Tsourakakis, C.E.: Toward quantifying vertex similarity in networks. Internet Math. 10(3–4), 263–286 (2014)
Yang, S.J.: Exploring complex networks by walking on them. Phys. Rev. E 71(1), 016107 (2005)
Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite network projection and personal recommendation. Phys. Rev. E 76(4), 1–7 (2007)
Zweig, K.A., Kaufmann, M.: A systematic approach to the one-mode projection of bipartite graphs. Soc. Netw. Anal. Min. 1(3), 187–218 (2011)
Acknowledgements
This work is partially supported by National Natural Science Foundation of China (NSFC) with grant numbers: 71532002, 61374177, 61373248.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Hou, L., Liu, K., Liu, J. (2018). Navigated Random Walks on Amazon Book Recommendation Network. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_75
Download citation
DOI: https://doi.org/10.1007/978-3-319-72150-7_75
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72149-1
Online ISBN: 978-3-319-72150-7
eBook Packages: EngineeringEngineering (R0)