Improving daily deals recommendation using explore-then-exploit strategies

Abstract

Daily-Deals Sites (DDSs) enable local businesses, such as restaurants and stores, to promote their products and services and to increase their sales by offering customers significantly reduced prices. If a customer finds a relevant deal in the catalog of electronic coupons, she can purchase it and the DDS receives a commission. Thus, offering relevant deals to customers maximizes the profitability of the DDS. An immediate strategy, therefore, would be to apply existing recommendation algorithms to suggest deals that are potentially relevant to specific customers, enabling more appealing, effective and personalized catalogs. However, this strategy may be innocuous because (1) most of the customers are sporadic bargain hunters, and thus past preference data is extremely sparse, (2) deals have a short living period, and thus data is extremely volatile, and (3) customers’ taste and interest may undergo temporal drifts. In order to address such a particularly challenging scenario, we propose a new algorithm for daily deals recommendation based on an explore-then-exploit strategy. Basically, we choose a fraction of the customers to gather feedback on the current catalog in the exploration phase, and the remaining customers to receive improved recommendations based on the previously gathered feedback in a posterior exploitation phase. During exploration, a co-purchase network structure is updated with customer feedback (i.e., the purchases of the day), and during exploitation the updated network is used to enrich the recommendation algorithm. An advantage of our approach is that it is agnostic to the underlying recommender algorithm. Using real data obtained from a large DDS in Brazil, we show that the way in which we split customers into exploration and exploitation impacts by large the effectiveness of the recommendations. We evaluate different splitting strategies based on network centrality metrics and show that our approach offers gains in mean average precision and mean reciprocal rank ranging from 14 to 34 % when applied on top of state-of-the-art recommendation algorithms.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Notes

  1. 1.

    http://www.groupon.com.

  2. 2.

    http://www.livingsocial.com.

  3. 3.

    http://www.peixeurbano.com.br.

  4. 4.

    Preliminary results on strategies for splitting customers into exploration and exploitation were presented by Lacerda et al. (2013) and are used as a baseline for our investigations in Sect. 4.

  5. 5.

    More specifically, we randomized the order in which deals are displayed to the customers, and a purchase is only considered if the purchased deal appears within the items that were originally seen by the customer.

  6. 6.

    Note that if the number of customers is not divisible by \(k\), the last chunk will contain less than \(k\) customers.

  7. 7.

    http://www.mymedialite.net.

  8. 8.

    http://graphlab.org/projects/graphchi.html.

  9. 9.

    http://www.netflix.com.

  10. 10.

    http://grouplens.org/datasets/movielens/.

  11. 11.

    Results in terms of MRR show similar trends and are omitted for brevity. A complete analysis in terms of both MAP and MRR is provided in Sects. 4.2.2 and 4.2.3.

  12. 12.

    http://www.snap.stanford.edu/snap/.

References

  1. Aizenberg, N., Koren, Y., & Somekh, O. (2012). Build your own music recommender by modeling internet radio streams. In Proceedings of the 21st international conference on world wide web, (pp. 1–10).

  2. Arabshahi, A. (2011). Undressing Groupon: an analysis of the Groupon business model. http://www.ahmadalia.com/blog/2011/01/undressing-groupon.html

  3. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2–3), 235–256.

    Article  MATH  Google Scholar 

  4. Azoulay-Schwartz, R., Kraus, S., & Wilkenfeld, J. (2004). Exploitation vs. exploration: Choosing a supplier in an environment of incomplete information. Decision Support Systems, 38(1), 1–18.

    Article  Google Scholar 

  5. Baeza-Yates, R., & Ribeiro-Neto, B. (2008). Modern information retrieval (2nd ed.). Addison-Wesley Publishing Company.

  6. Bellogin, A., Castells, P., & Cantador, I. (2011). Self-adjusting hybrid recommenders based on social network analysis. In Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, ACM. (pp. 1147–1148).

  7. Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: Massive online analysis. Journal of Machine Learning Research, 11(1), 1601–1604.

    Google Scholar 

  8. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.-U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4), 175–308.

    Article  MathSciNet  Google Scholar 

  9. Bonacich, P. (1987). Power and centrality: A family of measures. The American Journal of Sociology, 92(5), 1170–1182.

    Article  Google Scholar 

  10. Borgatti, S. (2005). A graph-theoretic perspective on centrality. Social Networks, 28(4), 466–484.

    Article  Google Scholar 

  11. Bottou, L., Peters, J., Candela, J., Charles, D. X., Chickering, M., Portugaly, E., et al. (2013). Counterfactual reasoning and learning systems: The example of computational advertising. Journal of Machine Learning Research, 14(1), 3207–3260.

    MATH  Google Scholar 

  12. Bouneffouf, D., Bouzeghoub, A., & Gançarski, A. L. (2012). A contextual-bandit algorithm for mobile context-aware recommender system. In Proceedings of the 19th international conference on neural information processing systems, (pp. 324–331).

  13. Byers, J., Mitzenmacher, M., & Zervas, G. (2012a). Daily deals: Prediction, social diffusion, and reputational ramifications. In Proceedings of the 5th ACM international conference on web search and data mining, (pp. 543–552).

  14. Byers, J. W., Mitzenmacher, M., Potamias, M., & Zervas, G. (2011). A month in the life of groupon. arXiv preprint arXiv:1105.0903.

  15. Byers, J. W., Mitzenmacher, M., & Zervas, G. (2012b). The Groupon effect on Yelp ratings: A root cause analysis. In Proceedings of the 13th ACM conference on electronic commerce, (pp. 248–265).

  16. Chakrabarti, D., Kumar, R., Radlinski, F., & Upfal, E. (2008). Mortal multi-armed bandits. In Proceedings of the 22nd annual conference on neural information processing systems, (pp. 273–280).

  17. Conry, D., Koren, Y., & Ramakrishnan, N. (2009). Recommender systems for the conference paper assignment problem. In Proceedings of the 3rd ACM conference on recommender systems, (pp. 357–360).

  18. Cremonesi, P., & Koren, Y. (2010). Performance of recommender algorithms on top-n recommendation tasks. ... conference on recommender ....

  19. Dholakia, U. M. (2010). How effective are Groupon promotions for business. http://www.ruf.rice.edu/~dholakia

  20. Edelman, B., Jaffe, S., & Kominers, S. (2011). To Groupon or not to Groupon: The profitability of deep discounts. Harvard Business School NOM unit working paper, (pp. 11–063).

  21. Even-Dar, E., Mannor, S., & Mansour, Y. (2006). Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. The Journal of Machine Learning Research, 7(1), 1079–1105.

    MATH  MathSciNet  Google Scholar 

  22. Feng, J., Bhargava, H., & Pennock, D. (2007). Implementing paid placement in web search engines: Computational evaluation of alternative mechanisms. INFORMS Journal on Computing, 19(1), 37–49.

    Article  Google Scholar 

  23. Freeman, L. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.

    Article  Google Scholar 

  24. Guha, S., & Munagala, K. (2007). Multi-armed bandits with limited exploration. In Proceedings of the annual symposium on theory of computing, (pp. 1–19).

  25. He, D., Chen, W., Wang, L., & Liu, T.-Y. (2013). Online learning for auction mechanism in bandit setting. Decision Support Systems, 56, 379–386.

    Article  Google Scholar 

  26. Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE international conference on data mining, (pp. 263–272).

  27. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4(1), 237–285.

    Google Scholar 

  28. Kauffman, R. J., & Wang, B. (2001). New buyers’ arrival under dynamic pricing market microstructure: The case of group-buying discounts on the internet. Journal of Management Information Systems, 18(2), 157–188.

    Google Scholar 

  29. Knuth, D. E. (1998). Sorting and searching. The art of computer programming (2nd ed., Vol. 3). Redwood City, CA: Addison Wesley Longman Publishing Co., Inc.

  30. Koenigstein, N., Dror, G., & Koren, Y. (2011). Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item taxonomy. In Proceedings of the 5th ACM conference on recommender systems, (pp. 165–172).

  31. Kohavi, R. (2012). Online controlled experiments: Introduction, learnings, and humbling statistics. In Proceedings of the 6th ACM conference on recommender systems, (pp. 1–2).

  32. Kohavi, R., Deng, A., Frasca, B., Longbotham, R., Walker, T., & Xu, Y. (2012). Trustworthy online controlled experiments: Five puzzling outcomes explained. In Proceedings of the 18th ACM international conference on knowledge discovery and data mining, (pp. 786–794).

  33. Koren, Y. (2010). Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data, 4(1), 1–24.

    Article  Google Scholar 

  34. Kumar, V., & Rajan, B. (2012). Social coupons as a marketing strategy: A multifaceted perspective. Journal of the Academy of Marketing Science, 40(1), 120–136.

    Article  Google Scholar 

  35. Lacerda, A., Veloso, A., & Ziviani, N. (2013). Exploratory and interactive daily deals recommendation. In Proceedings of the 7th ACM conference on recommender systems, (pp. 439–442).

  36. Lai, T.-L., & Yakowitz, S. (1995). Machine learning and nonparametric bandit theory. Automatic Control, IEEE Transactions on, 40(7), 1199–1209.

    Article  MATH  MathSciNet  Google Scholar 

  37. Landherr, A., Friedl, B., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business and Information Systems Engineering, 2(6), 371–385.

    Article  Google Scholar 

  38. Lappas, T., & Terzi, E. (2012). Daily-deal selection for revenue maximization. In Proceedings of the 21st ACM international conference on information and knowledge management, (pp. 565–574).

  39. Leskovec, J., & Faloutsos, C. (2006). Sampling from large graphs. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, (pp. 631–636).

  40. Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010a). A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on world wide web, (pp. 661–670).

  41. Li, L., Chu, W., Langford, J., & Wang, X. (2011). Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In Proceedings of the 4th ACM international conference on web search and data mining, (pp. 297–306).

  42. Li, W., Wang, X., Zhang, R., Cui, Y., Mao, J., & Jin, R. (2010b). Exploitation and exploration in a performance based contextual advertising system. In Proceedings of the 16th ACM international conference on knowledge discovery and data mining, (pp. 27–36).

  43. Liu, Y., Li, H., & Hu, F. (2013). Website attributes in urging online impulse purchase: An empirical investigation on consumer perceptions. Decision Support Systems, 55(3), 829–837.

    Article  MathSciNet  Google Scholar 

  44. Mahajan, D. K., Rastogi, R., Tiwari, C., & Mitra, A. (2012). Logucb: An explore-exploit algorithm for comments recommendation. In Proceedings of the 21st ACM international conference on information and knowledge management, (pp. 6–15).

  45. Maiya, A. S., & Berger-Wolf, T. Y. (2011). Benefits of bias: Towards better characterization of network sampling. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp. (105–113).

  46. Mannor, S., & Tsitsiklis, J. N. (2004). The sample complexity of exploration in the multi-armed bandit problem. The Journal of Machine Learning Research, 5, 623–648.

    MATH  MathSciNet  Google Scholar 

  47. Menezes, G., Almeida, J., Belém, F., Gonçalves, M., Lacerda, A., Moura, E., Pappa, G., Veloso, A., & Ziviani, N. (2010). Demand-driven tag recommendation. In Proceedings of the 2010 European conference on machine learning and knowledge discovery in databases, (pp. 402–417).

  48. Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., & Riedl, J. (2003). Movielens unplugged: Experiences with an occasionally connected recommender system. In Proceedings of the 8th international conference on intelligent user interfaces, (pp. 263–266).

  49. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report 1999–66, Stanford InfoLab.

  50. Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008). One-class collaborative filtering. In Proceedings of the 8th international conference on data mining, (pp. 502–511).

  51. Radlinski, F., Kleinberg, R., & Joachims, T. (2008). Learning diverse rankings with multi-armed bandits. In Proceedings of the 25th international conference on machine learning, (pp. 784–791).

  52. Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th conference on uncertainty in artificial intelligence, (pp. 452–461).

  53. Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (2010). Recommender systems handbook (1st ed.). New York, NY: Springer-Verlag New York, Inc.

  54. Robbins, H. (1952). Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 58(5), 527–535.

    Article  MATH  MathSciNet  Google Scholar 

  55. Rodriguez, M., Posse, C., & Zhang, E. (2012). Multiple objective optimization in recommender systems. In Proceedings of the 6th ACM conference on recommender systems (pp. 11–18).

  56. Schein, A., Popescul, A., & Ungar, L. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval.

  57. Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., & Hanjalic, A. (2012). CLiMF: Learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the 6th ACM conference on recommender systems (pp. 139–146).

  58. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction (Vol. 1). London: Cambridge University Press.

    Google Scholar 

  59. Tran-Thanh, L., Chapman, A., Munoz De Cote Flores Luna, J. E., Rogers, A., & Jennings, N. R. (2010). Epsilon-first policies for budget-limited multi-armed bandits. In Proceedings of the 24th AAAI conference on artificial intelligence, (pp. 1211–1219).

  60. Watkins, C. J. C. H. (1989). Learning from delayed rewards. PhD thesis.

  61. Ye, M., Sandholm, T., Wang, C., Aperjis, C., & Huberman, B. A. (2012). Collective attention and the dynamics of group deals. In Proceedings of the 21st international conference companion on world wide web, (pp. 1205–1212).

Download references

Acknowledgments

This work is partially supported by the National Institute of Science and Technology for the Web, MCT/CNPq Grant 57.3871/2008-6, and by the authors’ individual grants and scholarships from CAPES and CNPq. We thank Peixe Urbano for providing the data used in the experiments.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Rodrygo L. T. Santos.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lacerda, A., Santos, R.L.T., Veloso, A. et al. Improving daily deals recommendation using explore-then-exploit strategies. Inf Retrieval J 18, 95–122 (2015). https://doi.org/10.1007/s10791-014-9249-4

Download citation

Keywords

  • Daily-deals sites
  • Recommender systems
  • Armed bandit setting