36-462/662, Data Mining, Spring 2022
Lecture 23 (14 April 2022)
\[ \newcommand{\Expect}[1]{\mathbb{E}\left[ #1 \right]} \]
Solutions:
Expected revenue is \(q r_j p_{ij}\)
Prefers item \(j\) to item \(k\) when expected revenue is higher, \[ q r_j p_{ij} > q r_k p_{ik} ~ \Rightarrow ~ \frac{r_j}{r_k} > \frac{p_{ik}}{p_{ij}} \]
Even if \(p_{ik}=1 \gg p_{ij} \approx 0\), might still recommend \(j\) if \(r_j \gg r_k\)
[https://twitter.com/GabrielRossman/status/1169234703414484992]
(Prof. Rossman is joking, but he’s also an excellent sociologist of mass media and social diffusion, so this isn’t entirely a joke)
(If you want to learn to think this way, Shapiro and Varian (1998) is old but still excellent)
Goel, Sharad, Jake M. Hofman, and M. Irmak Sirer. 2012. “Who Does What on the Web: A Large-Scale Study of Browsing Behavior.” In Sixth International AAAI Conference on Weblogs and Social Media [ICWSM 2012], edited by John G. Breslin, Nicole B. Ellison, James G. Shanahan, and Zeynep Tufekci. AAAI Press. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4660.
Rubin, Donald B., and Richard P. Waterman. 2006. “Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology.” Statistical Science 21:206–22. https://doi.org/10.1214/088342306000000259.
Salganik, Matthew J., Peter S. Dodds, and Duncan J. Watts. 2006. “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market.” Science 311:854–56. http://www.princeton.edu/~mjs3/musiclab.shtml.
Salganik, Matthew J., and Duncan J. Watts. 2008. “Leading the Herd Astray: An Experimental Study of Self-Fulfilling Prophecies in an Artificial Cultural Market.” Social Psychological Quarterly 71:338–55. http://www.princeton.edu/~mjs3/salganik_watts08.pdf.
Shapiro, Carl, and Hal R. Varian. 1998. Information Rules: A Strategic Guide to the Network Economy. First. Boston: Harvard Business School Press.
Shardanand, Upendra, and Pattie Maes. 1995. “Social Information Filtering: Algorithms for Automating ‘Word of Mouth’.” In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems, 1:210–17. New York: ACM Press. https://doi.org/10.1145/223904.223931.
Sharma, Amit, Jake M. Hofman, and Duncan J. Watts. 2015. “Estimating the Causal Impact of Recommendation Systems from Observational Data.” In Proceedings of the Sixteenth ACM Conference on Economics and Computation [Ec ’15], edited by Michal Feldman, Michael Schwarz, and Tim Roughgarden, 453–70. New York: The Association for Computing Machinery. https://doi.org/10.1145/2764468.2764488.