Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search

Web search heavily relies on click-through behavior as an essential feedback signal for performance improvement and evaluation. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click (especially nonclick after examination) is regarded as a signal of irrelevance or uselessness. However, there are many cases where users do not click on any search results but still satisfy their information need with the contents of the results shown on the Search Engine Result Page (SERP). This raises the problem of measuring result usefulness and modeling user satisfaction in “Zero-click” search scenarios. Previous works have solved this issue by (1) detecting user satisfaction for abandoned SERP with context information and (2) considering result-level click necessity with external assessors’ annotations. However, few works have investigated the reason behind non-click behavior and estimated the usefulness of non-click results. A challenge for this research question is how to collect valuable feedback for non-click results. With neuroimaging technologies, we design a lab-based user study and reveal differences in brain signals while examining non-click search results with different usefulness levels. The findings in significant brain regions and electroencephalogram (EEG) spectrum also suggest that the process of usefulness judgment might involve similar cognitive functions of relevance perception and satisfaction decoding. Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i.e., content and context factors). Results show that it is feasible to utilize brain signals to improve usefulness estimation performance and enhancing human-computer interactions in “Zero-click” search scenarios.

[1]  Jaime Arguello Predicting Search Task Difficulty , 2014, ECIR.

[2]  Frank E. Pollick,et al.  When Relevance Judgement is Happening?: An EEG-based Study , 2015, SIGIR.

[3]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[4]  Yiqun Liu,et al.  When does Relevance Mean Usefulness and User Satisfaction in Web Search? , 2016, SIGIR.

[5]  Thierry Baccino,et al.  Decision-making in information seeking on texts: an eye-fixation-related potentials investigation , 2013, Front. Syst. Neurosci..

[6]  Thorsten Joachims,et al.  Evaluating Retrieval Performance Using Clickthrough Data , 2003, Text Mining.

[7]  Yiqun Liu,et al.  Understanding and Predicting Usefulness Judgment in Web Search , 2017, SIGIR.

[8]  Benjamin Blankertz,et al.  Towards a Cure for BCI Illiteracy , 2009, Brain Topography.

[9]  Dan Wu,et al.  Credibility assessment of good abandonment results in mobile search , 2020, Inf. Process. Manag..

[10]  Frank E. Pollick,et al.  Understanding Relevance: An fMRI Study , 2013, ECIR.

[11]  Fan Zhang,et al.  Evaluating Mobile Search with Height-Biased Gain , 2017, SIGIR.

[12]  Shaoping Ma,et al.  Constructing Click Models for Mobile Search , 2018, SIGIR.

[13]  Jing Wang,et al.  GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification , 2020, IJCAI.

[14]  Madian Khabsa,et al.  Learning to Account for Good Abandonment in Search Success Metrics , 2016, CIKM.

[15]  Bao-Liang Lu,et al.  Personalizing EEG-Based Affective Models with Transfer Learning , 2016, IJCAI.

[16]  Bao-Liang Lu,et al.  Differential entropy feature for EEG-based emotion classification , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[17]  Yiqun Liu,et al.  Relevance Estimation with Multiple Information Sources on Search Engine Result Pages , 2018, CIKM.

[18]  Jacek Gwizdka,et al.  Temporal dynamics of eye‐tracking and EEG during reading and relevance decisions , 2017, J. Assoc. Inf. Sci. Technol..

[19]  Tetsuya Sakai Towards zero-click mobile IR evaluation: knowing what and knowing when , 2012, SIGIR '12.

[20]  Frank E. Pollick,et al.  Understanding Information Need: An fMRI Study , 2016, SIGIR.

[21]  Yashar Moshfeghi,et al.  The Cortical Activity of Graded Relevance , 2020, SIGIR.

[22]  Madian Khabsa,et al.  Detecting Good Abandonment in Mobile Search , 2016, WWW.

[23]  Young-In Song,et al.  Click the search button and be happy: evaluating direct and immediate information access , 2011, CIKM '11.

[24]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[25]  Zhaoxiang Zhang,et al.  Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition , 2017, Cognitive Computation.

[26]  Li Tong,et al.  High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network , 2020, Frontiers in Human Neuroscience.

[27]  Jane Li,et al.  Good abandonment in mobile and PC internet search , 2009, SIGIR.

[28]  Ellen M. Voorhees,et al.  The Philosophy of Information Retrieval Evaluation , 2001, CLEF.

[29]  Ryen W. White,et al.  Leaving so soon?: understanding and predicting web search abandonment rationales , 2012, CIKM.

[30]  Yiqun Liu,et al.  Investigating Result Usefulness in Mobile Search , 2018, ECIR.

[31]  W. Klimesch EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[32]  Aapo Hyvärinen,et al.  New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit , 1997, NIPS.

[33]  Jing Wang,et al.  SST-EmotionNet: Spatial-Spectral-Temporal based Attention 3D Dense Network for EEG Emotion Recognition , 2020, ACM Multimedia.

[34]  Filip Radlinski,et al.  Query chains: learning to rank from implicit feedback , 2005, KDD '05.

[35]  Frank E. Pollick,et al.  Towards Predicting a Realisation of an Information Need based on Brain Signals , 2019, WWW.

[36]  Yang Song,et al.  Context-aware web search abandonment prediction , 2014, SIGIR.

[37]  Wenming Zheng,et al.  EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks , 2020, IEEE Transactions on Affective Computing.

[38]  Yong Ho Kim,et al.  ERP/MMR Algorithm for Classifying Topic‐Relevant and Topic‐Irrelevant Visual Shots of Documentary Videos , 2018, J. Assoc. Inf. Sci. Technol..

[39]  Yiqun Liu,et al.  Understanding Human Reading Comprehension with Brain Signals , 2021, ArXiv.

[40]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[41]  Lourens J. Waldorp,et al.  Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model , 2002, IEEE Transactions on Biomedical Engineering.