article

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

Authors:

Dietmar Jannach,

Lukas LercheAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 27, Issue 3-5

Pages 351 - 392

https://doi.org/10.1007/s11257-017-9194-1

Published: 01 December 2017 Publication History

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.

References

[1]

Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the stateof-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734-749 (2005).

Digital Library

[2]

Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook. Springer, pp. 217-253 (2011).

[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).

[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).

[5]

Anand, S., Mobasher, B.: Contextual recommendation. In: FromWeb to SocialWeb. Springer, pp. 142-160 (2007).

[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).

[7]

Berry, S., Levinsohn, J., Pakes, A.: Automobile prices in market equilibrium. Econometrica 63(4), 841-890 (1995).

[8]

Bonnin, G., Jannach, D.: Automated generation of music playlists: survey and experiments. ACM Comput. Surv. 47(2), 26:1-26:35 (2014).

Digital Library

[9]

Burke, R.: Knowledge-based recommender systems. Encyclop. Libr. Inf. Sci. 69(32), 180-200 (2000).

[10]

Candel, A., Parmar, V., LeDell, E., Arora, A.: Deep learning with H2O. 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).

Digital Library

[12]

Choudhary, V., Ghose, A., Mukhopadhyay, T., Rajan, U.: Personalized pricing and quality differentiation. Manag. Sci. 51(7), 1120-1130 (2005).

Digital Library

[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).

[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).

[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).

Digital Library

[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).

[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).

Digital Library

[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).

Digital Library

[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).

[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).

[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).

Digital Library

[24]

Jannach, D., Hegelich, K.: A case study on the effectiveness of recommendations in the mobile internet. In: Proceedings of the 3rd ACMConference on Recommender Systems, RecSys'09, pp. 205-208 (2009).

[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).

Digital Library

[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).

[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).

Digital Library

[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).

[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).

Digital Library

[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).

[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).

[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).

Digital Library

[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).

Digital Library

[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).

Digital Library

[35]

Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York, NY (2008).

[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).

[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).

[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).

Digital Library

[39]

Padmanabhan, P., Sadekar, K., Krishnan, G.: What's trending on netflix?. https://medium.com/netflixtechblog/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).

[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).

[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. 452-461.

[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).

Digital Library

[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).

Digital Library

[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).

[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).

Digital Library

[47]

Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265-1295 (2005).

Digital Library

[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).

[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).

[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).

[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).

Digital Library

[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).

[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).

Digital Library

[54]

Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR arXiv:1212.5701 (2012).

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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 27, Issue 3-5

December 2017

181 pages

ISSN:0924-1868

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Copyright © Copyright © 2017 Springer Science+Business Media B.V., part of Springer Nature.

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Kluwer Academic Publishers

United States

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Published: 01 December 2017

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Cited By

Solomon AKuflik TShapira BGuy I(2024)RecTemp: Temporal Reasoning in Recommendation SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687096(1227-1228)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687096
Ma YZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)The Effect of Proactive Cues on the Use of Decision Aids in Conversational Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665186(305-315)Online publication date: 27-Jun-2024
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Choi MKim HCho HLee JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Multi-intent-aware Session-based RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657928(2532-2536)Online publication date: 10-Jul-2024
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Bauer JJannach D(2024)Hybrid session-aware recommendation with feature-based modelsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09379-634:3(691-728)Online publication date: 1-Jul-2024
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Shi ZWang XLipani A(2024)Self Contrastive Learning for Session-Based RecommendationAdvances in Information Retrieval10.1007/978-3-031-56027-9_1(3-20)Online publication date: 24-Mar-2024
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Yang YZhang JWang YMiao ZTong Y(2023)Multiple Connectivity Views for Session-based RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608861(1000-1006)Online publication date: 14-Sep-2023
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