Cursor momentum for fascination measurement

Abstract

We present a very different cause of search engine user behaviors—fascination. It is generally identified as the initial effect of a product attribute on users’ interest and purchase intentions. Considering the fact that in most cases the cursor is driven directly by a hand to move via a mouse (or touchpad), we use the cursor movement as the critical feature to analyze the personal reaction against the fascinating search results. This paper provides a deep insight into the goal-directed cursor movement that occurs within a remarkably short period of time (<30 milliseconds), which is the interval between a user’s click-through and decision-making behaviors. Instead of the fundamentals, we focus on revealing the characteristics of the split-second cursor movement. Our empirical findings showed that a user may push or pull the mouse with a slightly greater strength when fascinated by a search result. As a result, the cursor slides toward the search result with an increased momentum. We model the momentum through a combination of translational and angular kinetic energy calculations. Based on Fitts’ law, we implement goal-directed cursor movement identification. Supported by the momentum, together with other physical features, we built different fascination-based search result reranking systems. Our experiments showed that goal-directed cursor momentum is an effective feature in detecting fascination. In particular, they show feasibility in both the personalized and cross-media cases. In addition, we detail the advantages and disadvantages of both click-through rate and cursor momentum for re-ranking search results.

Keywords

fascination measurement / user-oriented search / user behavior / goal-directed cursor movement / search result re-ranking

References

[1] Kahle L R, Homer P M. Physical attractiveness of the celebrity endorser: a social adaptation perspective. The Journal of Consumer Research, 1985, 11(4): 954–961

[2] Fitts P M. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 1954, 47(6): 381–391

[3] Brehm J W, Cohen A R. Explorations in Cognitive Dissonance. New York: Wiley, 1962

[4] Bem D J. Self-perception: an alternative interpretation of cognitive dissonance phenomena. Psychological Review, 1967, 74(3): 183–200 Thought, 1989, 212–252

[5] Chaiken S, Liberman A, Eagly A H. Heuristic and systematic information processing within and beyond the persuasion context. Unintended

[6] Bockenholt U, Albert D, Aschenbrenner M, Schmalhofer F. The effects of attractiveness, dominance, and attribute difference on information acquisition in multi attribute binary choice. Organizational Behavior and Human Decision Processes, 1991, 49(2): 258–281

[7] Fehr N V D, Stevik K. Persuasive advertising and product differentiation. Southern Economic Journal, 1998, 65(1): 113–126

[8] Saari T, Ravaja N, Laarni J, Turpeinen M, Kallinen K. Psychologically targeted persuasive advertising and product information in Ecommerce. In: Proceedings of the 6th International Conference on Electronic Commerce. 2004, 245–254

[9] Azimi J, Zhang R, Zhou Y, Navalpakkam V, Mao J, Feng X. Visual appearance of display Ads and its effect on click through rate. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012, 495–504

[10] Hong Y, Lu J, Yao J M, Zhu Q M, Zhou G D. What reviews are satisfactory: novel feature for automatic helpfulness voting. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012, 495–504

[11] Xu W, Manavoglu E, Cantu-Paz E. Temporal click model for sponsored search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010, 106–113

[12] Ceppi S, Gatti N, Gerding E H. Mechanism design for federated sponsored search auctions. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011, 608–613

[13] Jansen B J, Moore K, Carman S. Evaluating the performance of demographic targeting using gender in sponsored search. Information Processing & Management, 2013, 49(1): 286–302

[14] Ma H, Lyu M R, King I. Diversifying query suggestion results. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 1399–1404

[15] Jiang Q, Sun M. Fast query recommendation by search. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011, 1192–1197

[16] Chen Y, Yan T W. Position-normalized click prediction in search advertising. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 795–803

[17] Gao B, Yan J, Shen D, Liu T Y. Internet advertising: theory and practice. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2013, 1135

[18] Alipov V, Topinsky V, Trofimov L. On peculiarities of positional effects in sponsored search. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2014, 1015–1018

[19] Guo Q, Agichtein E. Ready to buy or just browsing? Detecting Web searcher goals from interaction data. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010, 130–137

[20] González-Caro C, Marcos M C. Different users and intents: an eyetracking analysis of Web search. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 9–12

[21] Buscher G, Dengel A, Biedert R, Elst L V. Attentive documents: eye tracking as implicit feedback for information retrieval and beyond. ACM Transactions on Interactive Intelligent Systems, 2012, 1(2): 9

[22] Toker D, Conati C, Steichen B, Carenini G. Individual user characteristics and information visualization: connecting the dots through eye tracking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2013, 295–304

[23] Wang G, Zhang X, Tang S, Zheng H, Zhao B Y. Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016, 225–236

[24] Borisov A, Markov I, de Rijke M, Serdyukov P. A context-aware time model for Web search. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2016, 205–214

[25] Wu Y, Liu Y, Su N, Ma S P, Ou W W. Predicting online shop-ping search satisfaction and user behaviors with electrodermal activity. In: Proceedings of the 26th International Conference on World Wide Web Companion. 2017, 855–856

[26] Navalpakkam V, Churchill E F. Mouse tracking: measuring and predicting users’ experience of Web-based content. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2012, 2963–2972

[27] Huang J, White R W, Dumais S. No clicks, no problem: using cursor movements to understand and improve search. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011, 1225–1234

[28] Huang J,White R W, Buscher G. User see, user point: gaze and cursor alignment in Web search. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2012, 1341–1350

[29] Gan K, Hoffman E R. Geometrical conditions for ballistic and visually controlled movements. Ergonomics, 1988, 31(5): 829–839

[30] Accot J, Zhai S. Beyond Fitts’ law: models for trajectory based HCI tasks. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems. 1997, 295–302

[31] Udwadia F, Kalaba R. Analytical Dynamics. Cambridge: Cambridge University Press, 1996

[32] Vapnik V, Golowich S E, Smola A. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 1997, 281–287

[33] Cao Y, Xu J, Liu T, Li H, Huang Y, Hon H. Adapting ranking SVM to document retrieval. In: Proceedings of the 29th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 186–193

[34] Quattoni A, Wang S, Morency L P, Collins M, Darrell M. Hidden conditional random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(10): 1848–1853

[35] Gao B, Yan J, Shen D, Liu T. Internet advertising: theory and practice. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2013, 1135–1135

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