Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

Article

Abstract

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer’s shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session.

Keywords

Recommender systems E-commerce Session-based Recommendation Context-aware Recommendation 

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook. Springer, pp. 217–253 (2011)Google Scholar
  3. Aghabozorgi, S.R., Wah, T.Y.: Recommender systems: incremental clustering on web log data. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, ICIS ’09, pp. 812–818 (2009)Google Scholar
  4. AlMurtadha, Y., Sulaiman, N.B., Mustapha, N., Udzir, N.I., Muda, Z.: ARS: web page recommendation system for anonymous users based on web usage mining. In: Proceedings of the European Conference of Systems, and European Conference of Circuits Technology and Devices, and European Conference of Communications, and European Conference on Computer Science, ECS’10/ECCTD’10/ECCOM’10/ECCS’10, pp. 115–120 (2010)Google Scholar
  5. Anand, S., Mobasher, B.: Contextual recommendation. In: From Web to Social Web. Springer, pp. 142–160 (2007)Google Scholar
  6. Anderson, A., Kumar, R., Tomkins, A., Vassilvitskii, S.: The dynamics of repeat consumption. In: Proceedings of the 23rd International Conference on World Wide Web, WWW’14, pp. 419–430 (2014)Google Scholar
  7. Berry, S., Levinsohn, J., Pakes, A.: Automobile prices in market equilibrium. Econometrica 63(4), 841–890 (1995)CrossRefMATHGoogle Scholar
  8. Bonnin, G., Jannach, D.: Automated generation of music playlists: survey and experiments. ACM Comput. Surv. 47(2), 26:1–26:35 (2014)CrossRefGoogle Scholar
  9. Burke, R.: Knowledge-based recommender systems. Encyclop. Libr. Inf. Sci. 69(32), 180–200 (2000)Google Scholar
  10. Candel, A., Parmar, V., LeDell, E., Arora, A.: Deep learning with \(\text{H}_{2}\text{ O }\). http://docs.h2o.ai/h2o/latest-stable/h2o-docs/booklets/DeepLearningBooklet.pdf. Accessed 17 August 2017
  11. Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist prediction via metric embedding. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’12, pp. 714–722 (2012)Google Scholar
  12. Choudhary, V., Ghose, A., Mukhopadhyay, T., Rajan, U.: Personalized pricing and quality differentiation. Manag. Sci. 51(7), 1120–1130 (2005)CrossRefGoogle Scholar
  13. Close, A.G., Kukar-Kinney, M.: Beyond buying: motivations behind consumers’ online shopping cart use. J Bus. Res. Adv. Internet Consum. Behav. Mark. Strategy 63(9–10), 986–992 (2010)Google Scholar
  14. Diehl, K., van Herpen, E., Lamberton, C.: Organizing products with complements versus substitutes: effects on store preferences as a function of effort and assortment perceptions. J. Retail. 91(1), 1–18 (2015)CrossRefGoogle Scholar
  15. Garcin, F., Dimitrakakis, C., Faltings, B.: Personalized news recommendation with context trees. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys’13, pp. 105–112 (2013)Google Scholar
  16. Garcin, F., Faltings, B., Donatsch, O., Alazzawi, A., Bruttin, C., Huber, A.: Offline and online evaluation of news recommender systems at swissinfo.ch. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys’14, pp. 169–176 (2014)Google Scholar
  17. Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. 6(4), 13:1–13:19 (2015)CrossRefGoogle Scholar
  18. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press. http://www.deeplearningbook.org. Accessed 17 August 2017 (2016)
  19. Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latent topic sequential patterns. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys’12, pp. 131–138 (2012)Google Scholar
  20. Hariri, N., Mobasher, B., Burke, R.: Context adaptation in interactive recommender systems. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys’14, pp. 41–48 (2014)Google Scholar
  21. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of the International Conference on Learning Representations, ICLR’16 (2016)Google Scholar
  22. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. CoRR arXiv:1207.0580 (2012)
  23. Jannach, D., Adomavicius, G.: Recommendations with a purpose. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys’16, pp. 7–10 (2016)Google Scholar
  24. Jannach, D., Hegelich, K.: A case study on the effectiveness of recommendations in the mobile internet. In: Proceedings of the 3rd ACM Conference on Recommender Systems, RecSys’09, pp. 205–208 (2009)Google Scholar
  25. Jannach, D., Ludewig, M.: Determining characteristics of successful recommendations from log data—a case study. In: Proceedings of the Symposium on Applied Computing, SAC’17, pp. 1643–1648 (2017a)Google Scholar
  26. Jannach, D., Ludewig, M.: When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems, RecSys’17, pp. 306–310 (2017b)Google Scholar
  27. Jannach, D., Lerche, L., Jugovac, M.: Adaptation and evaluation of recommendations for short-term shopping goals. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys’15, pp. 211–218 (2015a)Google Scholar
  28. Jannach, D., Lerche, L., Kamehkhosch, I.: Beyond “hitting the hits”—generating coherent music playlist continuations with the right tracks. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys’15, pp. 187–194 (2015b)Google Scholar
  29. Jannach, D., Lerche, L., Kamehkhosh, I., Jugovac, M.: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model. User Adapt Interact. 25(5), 427–491 (2015c)CrossRefGoogle Scholar
  30. Kamishima, T., Akaho, S.: Personalized pricing recommender system: multi-stage epsilon-greedy approach. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec’11, pp. 57–64 (2011)Google Scholar
  31. Kapoor, K., Kumar, V., Terveen, L., Konstan, J.A., Schrater, P.: I like to explore sometimes: adapting to dynamic user novelty preferences. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys’15, pp. 19–26 (2015)Google Scholar
  32. Lerche, L., Jannach, D., Ludewig, M.: On the value of reminders within e-commerce recommendations. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, UMAP’16, pp. 27–25 (2016)Google Scholar
  33. Li, L., Chu, W., Langford, J., Schapire, R.E.: A contextual-bandit approach to personalized news article recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW’10, pp. 661–670 (2010)Google Scholar
  34. Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI’10, pp. 31–40 (2010)Google Scholar
  35. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York, NY (2008)CrossRefMATHGoogle Scholar
  36. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Using sequential and non-sequential patterns in predictive web usage mining tasks. In: Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM’02, pp. 669–672 (2002)Google Scholar
  37. Moe, W.W.: Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream. J. Consum. Psychol. 13(1), 29–39 (2003)CrossRefGoogle Scholar
  38. Nguyen, Q.N., Ricci, F.: Long-term and session-specific user preferences in a mobile recommender system. In: Proceedings of the 13th International Conference on Intelligent User Interfaces, IUI’08, pp. 381–384 (2008)Google Scholar
  39. Padmanabhan, P., Sadekar, K., Krishnan, G.: What’s trending on netflix?. https://medium.com/netflix-techblog/whats-trending-on-netflix-f00b4b037f61. Accessed 17 August 2017 (2015)
  40. Plate, C., Basselin, N., Kröner, A., Schneider, M., Baldes, S., Dimitrova, V., Jameson, A.: Recomindation: New functions for augmented memories. In: Proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH’06, pp. 141–150 (2006)Google Scholar
  41. Prassas, G., Pramataris, K.C., Papaemmanouil, O., Doukidis, G.J.: A recommender system for online shopping based on past customer behaviour. In: Proceedings of the 14th BLED Electronic Commerce Conference, BLED’01, pp. 766–782 (2001)Google Scholar
  42. 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, UAI’09, pp. 452–461Google Scholar
  43. Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW’10, pp. 811–820 (2010)Google Scholar
  44. Ricci, F., Venturini, A., Cavada, D., Mirzadeh, N., Blaas, D., Nones, M.: Product recommendation with interactive query management and twofold similarity. In: Proceedings of the 5th International Conference on Case-Based Reasoning, ICCBR’03, pp. 479–493 (2003)Google Scholar
  45. Romov, P., Sokolov, E.: Recsys challenge 2015: ensemble learning with categorical features. In: Proceedings of the 2015 International ACM Recommender Systems Challenge, RecSys’15 Challenge, pp. 1:1–1:4 (2015)Google Scholar
  46. Schnabel, T., Bennett, P.N., Dumais, S.T., Joachims, T.: Using shortlists to support decision making and improve recommender system performance. In: Proceedings of the 25th International Conference on World Wide Web, WWW’16, pp. 987–997 (2016)Google Scholar
  47. Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265–1295 (2005)MATHMathSciNetGoogle Scholar
  48. Shen, E., Lieberman, H., Lam, F.: What am I gonna wear?: scenario-oriented recommendation. In: Proceedings of the 12th International Conference on Intelligent User Interfaces, IUI’07, pp. 365–368 (2007)Google Scholar
  49. Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on International Conference on Machine Learning, ICML’13, pp. 1139–1147 (2013)Google Scholar
  50. Tavakol, M., Brefeld, U.: Factored MDPs for detecting topics of user sessions. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys’14, pp. 33–40 (2014)Google Scholar
  51. Wager, S., Wang, S., Liang, P.: Dropout training as adaptive regularization. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS’13, pp. 351–359 (2013)Google Scholar
  52. Werro, N., Stormer, H., Meier, A.: Personalized discount—a fuzzy logic approach. In: Proceedings of the 5th IFIP Conference on e-Commerce, E-Business, and E-Government, I3E’05, pp. 375–387 (2005)Google Scholar
  53. Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’16, pp. 729–732 (2016)Google Scholar
  54. Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR arXiv:1212.5701 (2012)

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceTU DortmundDortmundGermany

Personalised recommendations