Abstract
In recommender systems, critiquing has been popularly applied as an effective approach to obtaining users’ feedback on recommended products. In order to reduce users’ efforts of creating critiquing criteria on their own, some systems have aimed at suggesting critiques for users to choose. How to accurately match system-suggested critiques to users’ intended feedback hence becomes a challenging issue. In this paper, we particularly take into account users’ eye movements on recommendations to infer their critiquing feedback. Based on a collection of real users’ eye-gaze data, we have demonstrated the approach’s feasibility of implicitly deriving users’ critiquing criteria. It hence indicates a promising direction of using eye-tracking technique to improve existing critique suggestion methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
\(P(h|e)=N(h \wedge e)/N(e)\), where N() denotes the number of observations within all compound critiques.
- 2.
\(H@K=\sum \nolimits _{c \in C} \frac{1_{rank(p_c)\le K}}{|C|}\) and \(MRR=\sum \nolimits _{c \in C} \frac{1}{rank(p_c)}\), where \(rank(p_c)\) denotes the rank of critiqued product \(p_c\) (in cycle c) within the top-K viewed products as sorted by a certain fixation metric.
- 3.
We use FC-p, TFD-p, and AFD-p to respectively denote the measures of fixation count, total fixation duration, and average fixation duration at product level.
- 4.
FC-a, TFD-a, and AFD-a respectively denote the measures of fixation count, total fixation duration, and average fixation duration at attribute level.
- 5.
\(Precision=\sum \nolimits _{k \in AC} \frac{|Pred(k)\cap R(k)|}{|Pred(k)|}/|AC|\), \(Recall=\sum \nolimits _{k \in AC} \frac{|Pred(k)\cap R(k)|}{|R(k)|}/|AC|\), \(F1=\sum \nolimits _{k \in AC} \frac{2 \times Precision(k) \times Recall(k)}{Precision(k)+Recall(k)}/|AC|\), and \(HitRaito=\frac{\sum \nolimits _{k \in AC} |Pred(k)\cap R(k)|}{q}\), where AC denotes the set of three critique options {“keep”, “improve”, “compromise”}, Pred(k) denotes the set of attributes that are inferred with critique k, R(k) contains attributes that are actually critiqued with k, and q is the total number of attribute critiques (that is 380 in our data).
- 6.
\(Lift(X \Rightarrow Y)=\frac{supp(X \cup Y)}{supp(X) \times supp(Y)}\), \(Confidence(X \Rightarrow Y)=\frac{supp(X \cup Y)}{supp(X)}\), where supp(X) gives the proportion of transactions that contain X.
- 7.
We set 0.5 as Confidence threshold, as it indicates a high probability that at least half of transactions contain the antecedent leading to the consequence.
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD 1993, pp. 207–216. ACM, New York (1993)
Bridge, D., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. Knowl. Eng. Rev. 20(3), 315–320 (2005)
Burke, R.D., Hammond, K.J., Young, B.: The findme approach to assisted browsing. IEEE Expert Intell. Syst. Appl. 12(4), 32–40 (1997)
Castagnos, S., Jones, N., Pu, P.: Eye-tracking product recommenders’ usage. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 29–36. ACM, New York (2010)
Chen, L., Pu, P.: Evaluating critiquing-based recommender agents. In: Proceedings of the 21st National Conference on Artificial Intelligence, AAAI 2006, vol. 1, pp. 157–162. AAAI Press (2006)
Chen, L., Pu, P.: Hybrid critiquing-based recommender systems. In: Proceedings of the 12th International Conference on Intelligent User Interfaces, IUI 2007, pp. 22–31. ACM (2007)
Chen, L., Pu, P.: Interaction design guidelines on critiquing-based recommender systems. User Model. User-Adap. Inter. 19(3), 167–206 (2009)
Chen, L., Pu, P.: Experiments on the preference-based organization interface in recommender systems. ACM Trans. Comput. Hum. Inter. 17(1), 1–33 (2010)
Chen, L., Pu, P.: Eye-tracking study of user behavior in recommender interfaces. In: Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 375–380. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13470-8_35
Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Model. User-Adap. Inter. 22(1–2), 125–150 (2012)
Cheng, S., Liu, X., Yan, P., Zhou, J., Sun, S.: Adaptive user interface of product recommendation based on eye-tracking. In: Proceedings of the 2010 Workshop on Eye Gaze in Intelligent Human Machine Interaction, EGIHMI 2010, pp. 94–101. ACM, New York (2010)
Ehmke, C., Wilson, S.: Identifying web usability problems from eye-tracking data. In: Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI..But Not As We Know It, BCS-HCI 2007, vol. 1, pp. 119–128. British Computer Society, Swinton (2007)
Giordano, D., Kavasidis, I., Pino, C., Spampinato, C.: Content based recommender system by using eye gaze data. In: Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA 2012, pp. 369–372. ACM, New York (2012)
Grasch, P., Felfernig, A., Reinfrank, F.: Recomment: towards critiquing-based recommendation with speech interaction. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 157–164. ACM, New York (2013)
Jung, J., Matsuba, Y., Mallipeddi, R., Funaya, H., Ikeda, K., Lee, M.: Evolutionary programming based recommendation system for online shopping. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific, pp. 1–4, October 2013
Linden, G., Hanks, S., Lesh, N.: Interactive assessment of user preference models: the automated travel assistant. In: Jameson, A., Paris, C., Tasso, C. (eds.) UM 1997. ICMS, vol. 383, pp. 67–78. Springer, Heidelberg (1997). doi:10.1007/978-3-7091-2670-7_9
Mahmood, T., Mujtaba, G., Venturini, A.: Dynamic personalization in conversational recommender systems. IseB 12(2), 213–238 (2014)
McCarthy, K., McGinty, L., Smyth, B., Reilly, J.: A live-user evaluation of incremental dynamic critiquing. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 339–352. Springer, Heidelberg (2005). doi:10.1007/11536406_27
McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Experiments in dynamic critiquing. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, IUI 2005, pp. 175–182. ACM (2005)
McCarthy, K., Salem, Y., Smyth, B.: Experience-based critiquing: reusing critiquing experiences to improve conversational recommendation. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS (LNAI), vol. 6176, pp. 480–494. Springer, Heidelberg (2010). doi:10.1007/978-3-642-14274-1_35
Poole, A., Ball, L.J.: Eye tracking in human-computer interaction and usability research: current status and future prospects. In: Ghaoui, C. (ed.) Encyclopedia of Human-Computer Interaction. Idea Group Inc., Pennsylvania (2005)
Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic critiquing. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 763–777. Springer, Heidelberg (2004). doi:10.1007/978-3-540-28631-8_55
Salvucci, D.D., Goldberg, J.H.: Identifying fixations and saccades in eye-tracking protocols. In: Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, ETRA 2000, pp. 71–78. ACM, New York (2000)
Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 32–41. ACM (2002)
Tullis, T., Albert, W.: Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics. Morgan Kaufmann Publishers Inc., San Francisco (2008)
Xie, H., Chen, L., Wang, F.: Collaborative compound critiquing. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 254–265. Springer, Heidelberg (2014). doi:10.1007/978-3-319-08786-3_22
Xu, S., Jiang, H., Lau, F.C.: Personalized online document, image and video recommendation via commodity eye-tracking. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, pp. 83–90. ACM, New York (2008)
Zhang, J., Pu, P.: A comparative study of compound critique generation in conversational recommender systems. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 234–243. Springer, Heidelberg (2006). doi:10.1007/11768012_25
Acknowledgments
We thank participants who took part in our experiment. We thank Dr. Weike Pan and Ms. Wai Yee Wong for assisting in data processing and analysis. We also thank Hong Kong RGC and China NSFC for sponsoring the described research work (under projects RGC/HKBU12200415 and NSFC/61272365).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Chen, L., Wang, F., Wu, W. (2016). Inferring Users’ Critiquing Feedback on Recommendations from Eye Movements. In: Goel, A., Díaz-Agudo, M., Roth-Berghofer, T. (eds) Case-Based Reasoning Research and Development. ICCBR 2016. Lecture Notes in Computer Science(), vol 9969. Springer, Cham. https://doi.org/10.1007/978-3-319-47096-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-47096-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47095-5
Online ISBN: 978-3-319-47096-2
eBook Packages: Computer ScienceComputer Science (R0)