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International Conference on Web Information Systems and Technologies

WEBIST 2018: Web Information Systems and Technologies pp 56–77Cite as

Personalization and the Conversational Web

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 372))

Abstract

Hyper-personalization intends to maximize the opportunities a marketer has to tailor content that fits each and every customer’s wants and needs. Naturally, gathering and analyzing more data is the key to those opportunities. This is were the “Conversation Web” comes in, which in the near future is expected to transform to so much more than just conversational interfaces (chat-bots). In a truly Conversation Web, websites and users implicitly “discuss” in the form of clicks, mouse scrolls and movements, as well as page views and product purchases. Websites use this information for decoding user interests and profile and provide customized one-to-one services. In this work we proposed an integrated architecture for the conversational Web; consequently we propose a novel hybrid approach for recommendations using offline and online analysis, as well as we propose a novel personalized search strategy that takes into account the strict time performance limitations applied in e-commerce. We evaluate the proposed methods on three different datasets and we show that our personalized search approach provides considerably improvements in search results while being suitable for near real-time search in commercial environments. Regarding personalized recommendations, the proposed approach outperforms current state-of-art methods in small-medium datasets and improves performance in large datasets when combined with other methods.

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Notes

  1. 1.

    http://diginetica.com/.

References

  1. Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 19–26. ACM, New York, NY, USA (2006). https://doi.org/10.1145/1148170.1148177

  2. Ben-Shimon, D., Tsikinovsky, A., Friedmann, M., Shapira, B., Rokach, L., Hoerle, J.: RecSys challenge 2015 and the YOOCHOOSE dataset. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, pp. 357–358. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2792838.2798723

  3. Bennett, P.N., et al.: Modeling the impact of short- and long-term behavior on search personalization. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012. pp. 185–194. ACM, New York, NY, USA (2012). https://doi.org/10.1145/2348283.2348312

  4. Birant, D.: Data mining using RFM analysis. In: Knowledge-Oriented Applications in Data Mining, chap. 6. Funatsu, Kimito, Rijeka (2011). https://doi.org/10.5772/13683

    Google Scholar 

  5. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003). http://dl.acm.org/citation.cfm?id=944919.944937

    MATH  Google Scholar 

  6. Burges, C., et al.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, ICML 2005, pp. 89–96. ACM, New York, NY, USA (2005). https://doi.org/10.1145/1102351.1102363

  7. Burke, R.: Hybrid recommender systems: survey and experiments. User Modeling User-Adap. Inter. 12(4), 331–370 (2002). https://doi.org/10.1023/A:1021240730564

    Article  MATH  Google Scholar 

  8. Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Modeling User-Adap. Inter. 22(1), 125–150 (2012). https://doi.org/10.1007/s11257-011-9108-6

    Article  Google Scholar 

  9. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785

  10. Chen, Y.L., Kuo, M.H., Wu, S.Y., Tang, K.: Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electron. Commer. Res. Appl. 8(5), 241–251 (2009). https://doi.org/10.1016/j.elerap.2009.03.002. Special Issue: Marketing and Electronic Commerce

    Article  Google Scholar 

  11. CIKM Cup organizing committee: Cikm cup 2016 track 2: Personalized e-commerce search challenge (2016). https://competitions.codalab.org/competitions/11161#learnthedetails-data2. Accessed 15 Jan 2019

  12. Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 233–240. ACM, New York, NY, USA (2006).https://doi.org/10.1145/1143844.1143874

  13. Fader, P.S., Hardie, B.G., Lee, K.L.: RFM and CLV: using iso-value curves for customer base analysis. J. Mark. Res. 42(4), 415–430 (2005). https://doi.org/10.1509/jmkr.2005.42.4.415

    Article  Google Scholar 

  14. Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: Developing constraint-based recommenders. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 187–215. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_6

    Chapter  Google Scholar 

  15. Gellert, A., Florea, A.: Web prefetching through efficient prediction by partial matching. World Wide Web 19(5), 921–932 (2016). https://doi.org/10.1007/s11280-015-0367-8

    Article  Google Scholar 

  16. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  17. Gormley, C., Tong, Z.: Elasticsearch: The Definitive Guide, 1st edn. O’Reilly Media Inc., Sebastopol (2015)

    Google Scholar 

  18. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. CoRR abs/1511.06939 (2015). http://arxiv.org/abs/1511.06939

  19. Huang, S.L.: Designing utility-based recommender systems for e-commerce: evaluation of preference-elicitation methods. Electron. Commer. Rec. Appl. 10(4), 398–407 (2011). https://doi.org/10.1016/j.elerap.2010.11.003

    Article  Google Scholar 

  20. Jannach, D., Ludewig, M.: Investigating personalized search in e-commerce. In: FLAIRS Conference, pp. 645–650. AAAI Press (2017)

    Google Scholar 

  21. Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 133–142. ACM, New York, NY, USA (2002). https://doi.org/10.1145/775047.775067

  22. Saleh, K.: Online shopping personalization - statistics and trends (2018). https://www.invespcro.com/blog/online-shopping-personalization/. Accessed 15 Jan 2019

  23. Kong, D.: Personalized feature based re-ranking method for ecommerce search at cikm cup 2016. Technical report, CIKM Cup (2016)

    Google Scholar 

  24. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009). https://doi.org/10.1109/MC.2009.263

    Article  Google Scholar 

  25. Liu, S., Xiao, F., Ou, W., Si, L.: Cascade ranking for operational e-commerce search. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 1557–1565. ACM, New York, NY, USA (2017). https://doi.org/10.1145/3097983.3098011

  26. Mahmood, T., Ricci, F.: Improving recommender systems with adaptive conversational strategies. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT 2009, pp. 73–82. ACM, New York, NY, USA (2009). https://doi.acm.org/10.1145/1557914.1557930

  27. McCandless, M., Hatcher, E., Gospodnetic, O.: Lucene in Action, Second Edition: Covers Apache Lucene 3.0. Manning Publications Co., Greenwich (2010)

    Google Scholar 

  28. Palotti, J.: Learning to rank for personalized e-commerce search at CIKM cup 2016. Technical report CIKM Cup (2016)

    Google Scholar 

  29. Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10

    Chapter  Google Scholar 

  30. Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, 27–31 August 2017, pp. 130–137 (2017). https://doi.acm.org/10.1145/3109859.3109896

  31. Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr. 3(4), 333–389 (2009). https://doi.org/10.1561/1500000019

    Article  Google Scholar 

  32. Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 735–767. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_23

    Chapter  Google Scholar 

  33. Salimans, T., Paquet, U., Graepel, T.: Collaborative learning of preference rankings. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 261–264. ACM, New York, NY, USA (2012). https://doi.acm.org/10.1145/2365952.2366009

  34. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285–295. ACM, New York, NY, USA (2001). https://doi.org/10.1145/371920.372071

  35. Speretta, M., Gauch, S.: Personalized search based on user search histories. In: The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), pp. 622–628, September 2005. https://doi.org/10.1109/WI.2005.114

  36. Sun, A., Lim, E.P., Ng, W.K.: Web classification using support vector machine. In: Proceedings of the 4th International Workshop on Web Information and Data Management, WIDM 2002, pp. 96–99. ACM, New York, NY, USA (2002). https://doi.acm.org/10.1145/584931.584952

  37. Teevan, J., Dumais, S.T., Liebling, D.J.: To personalize or not to personalize: Modeling queries with variation in user intent. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 163–170. ACM, New York, NY, USA (2008). https://doi.org/10.1145/1390334.1390364

  38. Vavliakis, K.N., Kotouza, M.T., Symeonidis, A.L., Mitkas, P.A.: Recommendation systems in a conversational web. In: Proceedings of the 14th International Conference on Web Information Systems and Technologies, WEBIST 2018, Seville, Spain, 18–20 September 2018, pp. 68–77 (2018). https://doi.org/10.5220/0006935300680077

  39. Wang, Y., Wang, L., Li, Y., He, D., Liu, T.Y., Chen, W.: A theoretical analysis of NDCG type ranking measures. CoRR abs/1304.6480 (2013)

    Google Scholar 

  40. Witten, D.M., Tibshirani, R.: A framework for feature selection in clustering. J. Am. Stat. Assoc. 105(490), 713–726 (2010)

    Article  MathSciNet  Google Scholar 

  41. Ying, Y., Feinberg, F., Wedel, M.: Leveraging missing ratings to improve online recommendation systems. J. Mark. Res. 43(3), 355–365 (2006). http://www.jstor.org/stable/30162410

    Article  Google Scholar 

  42. Yu, J., Mohan, S., Putthividhya, D.P., Wong, W.K.: Latent Dirichlet allocation based diversified retrieval for e-commerce search. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM 2014, pp. 463–472. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2556195.2556215

  43. Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. CoRR abs/1707.07435 (2017)

    Google Scholar 

  44. Zhao, X., Zhang, W., Wang, J.: Interactive collaborative filtering. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM 2013, pp. 1411–1420. ACM, New York, NY, USA (2013). https://doi.acm.org/10.1145/2505515.2505690

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Acknowledgements

This work was partially funded by an IKY scholarship funded by the “Strengthening of Post-Academic Researchers” Act from the resources of the OP “Human Resources Development, Education and Lifelong Learning” with Priority Axes 6, 8, 9 and co-funded by the European Social Fund ECB and the Greek government. The authors would like to thank George Katsikopoulos for his valuable help with the personalized search experiments and Kostas Nikolaros for his useful feedback regarding user search behavior.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR54124, Thessaloniki, Greece

    Konstantinos N. Vavliakis, Maria Th. Kotouza, Andreas L. Symeonidis & Pericles A. Mitkas

  2. Pharm24.gr, GR23057, Dafni, Lakonias, Greece

    Konstantinos N. Vavliakis

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  1. Konstantinos N. Vavliakis
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  2. Maria Th. Kotouza
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Correspondence to Konstantinos N. Vavliakis .

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Editors and Affiliations

  1. University of Seville, Seville, Spain

    María José Escalona

  2. University of Seville, Seville, Spain

    Francisco Domínguez Mayo

  3. University of Agder, Kristiansand, Norway

    Tim A. Majchrzak

  4. University of Paris 1, Paris, France

    Valérie Monfort

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Vavliakis, K.N., Kotouza, M.T., Symeonidis, A.L., Mitkas, P.A. (2019). Personalization and the Conversational Web. In: Escalona, M., Domínguez Mayo, F., Majchrzak, T., Monfort, V. (eds) Web Information Systems and Technologies. WEBIST 2018. Lecture Notes in Business Information Processing, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-35330-8_4

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  • DOI: https://doi.org/10.1007/978-3-030-35330-8_4

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