Advertisement

Improving the User Experience with a Conversational Recommender System

  • Fedelucio Narducci
  • Marco de Gemmis
  • Pasquale Lops
  • Giovanni Semeraro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)

Abstract

Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. Generally, they have an interaction with users based on natural language, buttons, or both. In this paper we study the user interaction with a content-based recommender system implemented as a Telegram chatbot. More specifically, we investigate on one hand what are the best strategies for reducing the cost of interaction for the users and, on the other hand how to improve their experience. Our chatbot is able to provide personalized recommendations in the movie domain and implements critiquing strategies for improving the recommendation accuracy as well. In a preliminary experimental evaluation, carried out through a user study, interesting results emerged.

Keywords

Conversational recommender system Chatbot User experience 

Notes

Acknowledgment

This work has been funded by the projects UNIFIED WEALTH MANAGEMENT PLATFORM - OBJECTWAY SpA - Via Giovanni Da Procida nr. 24, 20149 MILANO - c.f., P. IVA 07114250967, and PON01 00850 ASK-Health (Advanced system for the interpretations and sharing of knowledge in health care).

References

  1. 1.
    Mahmood, T., Ricci, F.: Improving recommender systems with adaptive conversational strategies. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, pp. 73–82. ACM (2009)Google Scholar
  2. 2.
    Mcginty, L., Smyth, B.: Adaptive selection: an analysis of critiquing and preference-based feedback in conversational recommender systems. Int. J. Electron. Commer. 11(2), 35–57 (2006)CrossRefGoogle Scholar
  3. 3.
    Lops, P., De Gemmis, M., Semeraro, G., Narducci, F., Musto, C.: Leveraging the LinkedIn social network data for extracting content-based user profiles. In: RecSys 2011 - Proceedings of the 5th ACM Conference on Recommender Systems, pp. 293–296 (2011)Google Scholar
  4. 4.
    Basile, P., Musto, C., de Gemmis, M., Lops, P., Narducci, F., Semeraro, G.: Content-based recommender systems + DBpedia knowledge = semantics-aware recommender systems. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 163–169. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12024-9_21CrossRefGoogle Scholar
  5. 5.
    Musto, C., Narducci, F., Lops, P., de Gemmis, M.: Combining collaborative and content-based techniques for tag recommendation. In: Buccafurri, F., Semeraro, G. (eds.) EC-Web 2010. LNBIP, vol. 61, pp. 13–23. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15208-5_2CrossRefGoogle Scholar
  6. 6.
    Felfernig, A., Burke, R., Pu, P.: Preface to the special issue on user interfaces for recommender systems. User Model. User-Adapt. Interact. 22(4), 313–316 (2012)CrossRefGoogle Scholar
  7. 7.
    Narducci, F., Musto, C., Semeraro, G., Lops, P., de Gemmis, M.: Leveraging encyclopedic knowledge for transparent and serendipitous user profiles. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 350–352. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38844-6_36CrossRefGoogle Scholar
  8. 8.
    Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Model. User-Adapt. Interact. 22(1–2), 125–150 (2012)CrossRefGoogle Scholar
  9. 9.
    Berkovsky, S., Freyne, J., Oinas-Kukkonen, H.: Influencing individually: fusing personalization and persuasion. ACM Trans. Interact. Intell. Syst. (TiiS) 2(2), 9 (2012)Google Scholar
  10. 10.
    Tintarev, N., Masthoff, J.: Evaluating the effectiveness of explanations for recommender systems. User Model. User-Adapt. Interact. 22(4–5), 399–439 (2012)CrossRefGoogle Scholar
  11. 11.
    Kveton, B., Berkovsky, S.: Minimal interaction content discovery in recommender systems. ACM Trans. Interact. Intell. Syst. (TiiS) 6(2), 15 (2016)Google Scholar
  12. 12.
    Sun, Y., Zhang, Y., Chen, Y., Jin, R.: Conversational recommendation system with unsupervised learning. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 397–398. ACM (2016)Google Scholar
  13. 13.
    Christakopoulou, K., Radlinski, F., Hofmann, K.: Towards conversational recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 815–824. ACM (2016)Google Scholar
  14. 14.
    Smyth, B., McGinty, L., Reilly, J., McCarthy, K.: Compound critiques for conversational recommender systems. In: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004, pp. 145–151. IEEE Computer Society, Washington, DC (2004).  https://doi.org/10.1109/WI.2004.45
  15. 15.
    Yujian, L., Bo, L.: A normalized levenshtein distance metric. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1091–1095 (2007)CrossRefGoogle Scholar
  16. 16.
    Haveliwala, T.H.: Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRefGoogle Scholar
  17. 17.
    Musto, C., Lops, P., Basile, P., de Gemmis, M., Semeraro, G.: Semantics-aware graph-based recommender systems exploiting linked open data. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 229–237. ACM (2016)Google Scholar
  18. 18.
    Musto, C., Narducci, F., Lops, P., De Gemmis, M., Semeraro, G.: ExpLOD: a framework for explaining recommendations based on the linked open data cloud. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 151–154. ACM (2016)Google Scholar
  19. 19.
    Knijnenburg, B.P., Willemsen, M.C.: Evaluating recommender systems with user experiments. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 309–352. Springer, Boston (2015).  https://doi.org/10.1007/978-1-4899-7637-6_9CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fedelucio Narducci
    • 1
  • Marco de Gemmis
    • 1
  • Pasquale Lops
    • 1
  • Giovanni Semeraro
    • 1
  1. 1.Department of Computer ScienceUniversity of Bari Aldo MoroBariItaly

Personalised recommendations