Capturing User Preferences Through Interactive Visualization to Improve Recommendations

  • Pooja Vashisth
  • Purnima KhuranaEmail author
  • Punam Bedi
  • Sumit Kr Agarwal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 899)


Recommender systems are widely used intelligent applications which assist users in a decision-making process to select one product from a huge set of alternative products or services. Recent research focus has been on developing methods for generating recommendations. We note lack of coherent research in the field of visual depictions of recommendations as well as how visualization and interactivity can aid users in use and decision-making with recommender systems. This paper proposes a novel approach to visualization of recommendations driven by preferences of the user to provide them with beneficial and persuasive recommendations. A personalized visual interface has been used considering requirements of the user and product’s utility in order to provide effective recommendations to the user. The anticipated visual interface is interactive and tailored according to user’s preferences. Users can alter their current necessities interactively in order to acquire the value-added recommendations from the Recommender System. It provides the users with a simple and most relevant set of recommendations as per their interest. This substantially reduces users’ interaction effort, especially for a sizable and complex product domain. Precision and recall metrics have been used to measure performance of the proposed system as well as users’ subjective feedback.


Recommender system Visualization Interaction Personalization Preferences 


  1. 1.
    Analytis, P., Schnabel, T., Herzog, S., Barkoczi, D., Joachims, T.: A preference elicitation interface for collecting dense recommender datasets with rich user information. In: Recsys 2017, Como, Italy (2017)Google Scholar
  2. 2.
    Armentano, M., Abalde, R., Schiaffino, S., Amandi, A.: User acceptance of recommender systems: influence of the preference elicitation algorithm. In: Proceedings of Semantic and Social Media Adaptation and Personalization (2014)Google Scholar
  3. 3.
    Bedi, P., Banati, H.: Trust aware usability. Spec. Issue Website Eval. J. Inf. Technol. Tourism 8(3), 215–226 (2006)CrossRefGoogle Scholar
  4. 4.
    Bedi, P., Banati, H.: Assessing user trust to improve web usability. J. Comput. Sci. 2(3), 283–287 (2006)CrossRefGoogle Scholar
  5. 5.
    Bedi, P., Sinha, A., Agarwal, S., Awasthi, A., Prasad, G., Saini, D.: Influence of Terrain on modern tactical combat: trust-based recommender system. Defense Sci. J. 60(4), 405–411 (2010)Google Scholar
  6. 6.
    Bedi, P., Agarwal, S.: AORS: aspect-oriented recommender system. In: Proceedings CSNT 2011 - The International Conference on Communication Systems and Network Technologies, Jammu, India, 03–05 June, pp. 709–713. IEEE Computer Society, USA (2011a)Google Scholar
  7. 7.
    Bedi, P., Agarwal, S.: Preference learning in aspect oriented recommender system. In: CICN 2011 (2011b)Google Scholar
  8. 8.
    Berkovsky, S., Freyne, J., Oinas-Kukkonen, H.: Influencing individually: fusing personalization and persuasion. ACM Trans. Interact. Intell. Syst. (TiiS) 2(2), article no. 9 (2012)Google Scholar
  9. 9.
    Burke, R.D., Hammond, K.J., Young, B.C.: The FindMe approach to assisted browsing. IEEE Expert Intell. Syst. Their Appl. 12(4), 32–40 (1997)Google Scholar
  10. 10.
    Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Model. User-Adap. Inter. 22, 125–150 (2012)CrossRefGoogle Scholar
  11. 11.
    Chesñevar, C., Maguitman, A.G., González, M.P.: Empowering recommendation technologies through argumentation. In: Simari, G., Rahwan, I. (eds.) Argumentation in Artificial Intelligence, pp. 403–422. Springer, Boston (2009).
  12. 12.
    Constantinides, M., Dowell, J.: User Interface Personalization in news apps. In: INRA Workshop, Halifax, Canada (2016)Google Scholar
  13. 13.
    Cremonesi, P., Garzotto, F., Turrin, R.: Investigating the persuasion potential of recommender systems from a quality perspective: an empirical study. ACM Trans. Interact. Intell. Syst. (TiiS), 2(2), article no. 11 (2012)Google Scholar
  14. 14.
    He, C., Paara, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)CrossRefGoogle Scholar
  15. 15.
    Islam, M., Ding, C., Chi, C., Personalized recommender system on whom to follow in Twitter. In: Proceedings of Big Data and Cloud Computing, (IEEE Fourth International Conference) (2014)Google Scholar
  16. 16.
    Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic Personalization in E-Learning Based on Recommendation Systems: An Overview (2011)Google Scholar
  17. 17.
    Pu, P., Chen, L.: A user-centric evaluation framework of recommender systems. In: Proceedings of the ACM RecSys 2010 Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces (UCERSTI), Barcelona, Spain, Published by (2010). ISSN 1613-0073Google Scholar
  18. 18.
    Puerta, A.R.: A model-based interface development environment. IEEE Softw 14(4), 40–47 (1997)CrossRefGoogle Scholar
  19. 19.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011)Google Scholar
  20. 20.
    Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. In: Applications of Data Mining to Electronic Commerce, pp. 115–153. Springer US (2001)Google Scholar
  21. 21.
    Sharma, A., Yan, B.: Pairwise learning in recommendation: experiments with community recommendation on linkedin. In: Proceedings of the 7th ACM Conference on Recommender systems, pp. 193–200 (2013)Google Scholar
  22. 22.
    Sivapalan, S., Sadeghian, A., Rahnama, H.: Recommender systems in E-Commerce. In: Proceedings of the World Automation Congress (2014)Google Scholar
  23. 23.
    Van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)Google Scholar
  24. 24.
    Vashisth, P., Bedi, P.: Interest-based personalized recommender system. In: Information and Communication Technologies (WICT), World Congress, pp. 245–250. IEEE, December 2011Google Scholar
  25. 25.
    Webb, G.I., Pazzani, M.J., Billsus, D.: Machine learning for user modeling. User Model. User-Adapt. Interact. 11(1–2), 19–29 (2001)Google Scholar
  26. 26.
    Yu, Y., He, J.: An analysis of users’ cognitive factors towards icon in interactive interface. In: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, (2010).

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Pooja Vashisth
    • 1
  • Purnima Khurana
    • 1
    Email author
  • Punam Bedi
    • 1
  • Sumit Kr Agarwal
    • 1
  1. 1.Department of Computer ScienceUniversity of DelhiDelhiIndia

Personalised recommendations