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Personalization and the Conversational Web

  • Konstantinos N. VavliakisEmail author
  • Maria Th. Kotouza
  • Andreas L. Symeonidis
  • Pericles A. Mitkas
Conference paper
Part of the Lecture Notes in Business Information Processing book series (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.

Keywords

Personalization Recommendation Search Elasticsearch Conversational web e-Commerce RFM Recurrent neural networks 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Konstantinos N. Vavliakis
    • 1
    • 2
    Email author
  • Maria Th. Kotouza
    • 1
  • Andreas L. Symeonidis
    • 1
  • Pericles A. Mitkas
    • 1
  1. 1.Department of Electrical and Computer Engineering Aristotle University of ThessalonikiThessalonikiGreece
  2. 2.Pharm24.grDafniGreece

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