Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking
Abstract
:Featured Application
Abstract
1. Introduction
2. Assumptions and Methodology
3. Experimental Results
3.1. Implicit Event Tracking
3.2. Recommending Interface Quality Parameters
3.3. Results
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Tomaszewska, A.; Jankowski, J.; Sałabun, W.; Wątróbski, J. Multicriteria Selection of Online Advertising Content for the Habituation Effect Reduction. In Intelligent Information and Database Systems; Springer: Cham, Switzerland, 2019. [Google Scholar]
- Kelly, D. Implicit Feedback: Using Behavior to Infer Relevance. In New Directions in Cognitive Information Retrieval. The Information Retrieval Series; Spink, A., Cole, C., Eds.; Springer: Dordrecht, Germany, 2006; Volume 19, Section IV; pp. 169–186. [Google Scholar]
- Sulikowski, P.; Zdziebko, T.; Turzyński, D.; Kańtoch, E. Human-website interaction monitoring in recommender systems. Procedia Comput. Sci. 2018, 126, 1587–1596. [Google Scholar] [CrossRef]
- Sulikowski, P.; Zdziebko, T.; Turzyński, D. Modeling online user product interest for recommender systems and ergonomics studies. Concurr. Comput. Pract. Exp. 2019, 31, e4301. [Google Scholar] [CrossRef]
- Zdziebko, T.; Sulikowski, P. Monitoring Human Website Interactions for Online Stores. In New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing; Rocha, A., Correia, A., Costanzo, S., Reis, L., Eds.; Springer: Cham, Switzerland, 2015; Volume 354, pp. 375–384. [Google Scholar]
- Wątróbski, J.; Jankowski, J.; Karczmarczyk, A.; Ziemba, A.P. Integration of Eye-Tracking Based Studies into e-Commerce Websites Evaluation Process with eQual and TOPSIS Methods. In Proceedings of the Information Systems: Research, Development, Applications, Education. 10th SIGSAND/PLAIS EuroSymposium 2017, Gdańsk, Poland, 22 September 2017. [Google Scholar]
- Jankowski, J.; Ziemba, P.; Wątróbski, J.; Kazienko, P. Towards the Tradeoff Between Online Marketing Resources Exploitation and the User Experience with the Use of Eye Tracking. In Proceedings of the Intelligent Information and Database Systems. 8th Asian Conference, ACIIDS 2016, Da Nang, Vietnam, 14–16 March 2016; Part I. Lecture Notes in Artificial Intelligence, Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.P., Eds.; Springer: Berlin, Germany, 2016; Volume 9621, pp. 330–343. [Google Scholar]
- Kluza, K.; Baran, M.; Bobek, S.; Nalepa, G.J. Overview of recommendation techniques in business process modeling. Proc. Knowl. Eng. Softw. Eng. (KESE) 2013, 2013, 46. [Google Scholar]
- Dyczkowski, K.; Stachowiak, A. A Recommender System with Uncertainty on the Example of Political Elections. In Advances in Computational Intelligence, Communications in Computer and Information Science; Springer: Berlin/Heidelberg, Germany, 2012; Volume 298, pp. 441–449. [Google Scholar]
- Geuens, S.; Coussement, K.; de Bock, K.W. A framework for configuring collaborative filtering-based recommendations derived from purchase data. Eur. J. Oper. Res. 2018, 256, 208–218. [Google Scholar] [CrossRef]
- Bobadilla, J.; Ortega, F.; Hernando, A.; Gutierrez, A. Recommender systems survey. Knowl.-Based Syst. 2013, 46, 109–132. [Google Scholar] [CrossRef]
- Lu, J.; Wu, D.; Mao, M.; Wang, W.; Zhang, G. Recommender system application developments: A survey. Decis. Support Syst. 2015, 74, 12–32. [Google Scholar] [CrossRef]
- Yera, R.; Martínez, L. Fuzzy Tools in Recommender Systems: A Survey. Int. J. Comput. Intell. Syst. 2017, 10, 776–803. [Google Scholar] [CrossRef] [Green Version]
- Ahirwadkar, B.; Deshmukh, S.N. Deep Neural Networks for Recommender Systems. Int. J. Innov. Technol. Explor. Eng. 2019, 8, 4838–4842. [Google Scholar]
- Zhang, S.; Yao, L.; Sun, A.; Tay, Y. Deep Learning Based Recommender System. ACM Comput. Surv. 2019, 52, 1–38. [Google Scholar] [CrossRef] [Green Version]
- Ricci, F.; Rokach, L.; Shapira, B.; Kantor, P.B. (Eds.) Recommender Systems Handbook; Springer: New York, NY, USA, 2010. [Google Scholar]
- Chen, L.; Pu, P. Eye-tracking study of user behavior in recommender interfaces. In International Conference on User Modeling, Adaptation, and Personalization; Springer: Berlin/Heidelberg, Germany, 2010; pp. 375–380. [Google Scholar]
- Fahim Shahriar, A.B.M.; Zaman Moon, M.; Mahmud, H.; Hasan, K. Online Product Recommendation System by Using Eye Gaze Data. In Proceedings of the International Conference on Computing Advancements (ICCA 2020). Association for Computing Machinery, Dhaka, Bangladesh, 10–12 January 2020; Article 61; pp. 1–7. [Google Scholar]
- Pu, P.; Chen, L.; Kumar, P. Evaluating Product Search and Recommender Systems for E-Commerce Environments. Electron. Commer. Res. 2008, 8, 1–27. [Google Scholar] [CrossRef] [Green Version]
- Bortko, K.; Bartków, P.; Jankowski, J.; Kuras, D.; Sulikowski, P. Multi-criteria Evaluation of Recommending Interfaces towards Habituation Reduction and Limited Negative Impact on User Experience. Procedia Comput. Sci. 2019, 159, 2240–2248. [Google Scholar] [CrossRef]
- Portnoy, F.; Marchionini, G. Modeling the effect of habituation on banner blindness as a function of repetition and search type: Gap analysis for future work. In CHI’10 Extended Abstracts on Human Factors in Computing Systems; ACM: New York, NY, USA, 2010; pp. 4297–4302. [Google Scholar]
- Ha, L.; McCann, K. An integrated model of advertising clutter in offline and online media. Int. J. Advert. 2008, 27, 569–592. [Google Scholar] [CrossRef]
- Jin, X.; Zhou, Y.; Mobasher, B. Task-oriented web user modeling for recommendation. In International Conference on User Modeling; Springer: Berlin/Heidelberg, Germany, 2005; pp. 109–118. [Google Scholar]
- Jankowski, J.; Hamari, J.; Watróbski, J. A gradual approach for maximising user conversion without compromising experience with high visual intensity website elements. Internet Res. 2019, 29, 194–217. [Google Scholar] [CrossRef]
- Rohrer, C.; Boyd, J. The rise of intrusive online advertising and the response of user experience research at yahoo! In CHI’04Extended Abstracts on Human Factors in Computing Systems; ACM: New York, NY, USA, 2004; pp. 1085–1086. [Google Scholar]
- Jankowski, J. Modeling the Structure of Recommending Interfaces with Adjustable Influence on Users. In Intelligent Information and Database Systems, Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2013; Volume 7803, pp. 429–438. [Google Scholar]
- Jannach, D.; Lerche, L.; Zanker, M. Recommending Based on Implicit Feedback. In Social Information Access. Lecture Notes in Computer Science; Brusilovsky, P., He, D., Eds.; Springer: Cham, Switzerland, 2018; Volume 10100, pp. 510–569. [Google Scholar]
- Lerche, L. Using Implicit Feedback for Recommender Systems: Characteristics, Applications, and Challenges. Ph.D. Thesis, Technische Universität Dortmund, Dortmund, Germany, December 2016. [Google Scholar]
- Zhao, Q.; Harper, F.M.; Adomavicius, G.; Konstan, J.A. Explicit or implicit feedback? Engagement or satisfaction?: A field experiment on machine-learning-based recommender systems. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France, 9–13 April 2018; ACM: New York, NY, USA, 2018; pp. 1331–1340. [Google Scholar]
- Zhou, M.; Ding, Z.; Tang, J.; Yin, D. Micro behaviors: A new perspective in e-commerce recommender systems. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Los Angeles, CA, USA, 5–9 February 2018; pp. 727–735. [Google Scholar]
- Tian, G.; Wang, J.; He, K.; Sun, C.; Tian, Y. Integrating implicit feedbacks for time-aware web service recommendations. Inf. Syst. Front. 2017, 19, 75–89. [Google Scholar] [CrossRef]
- Liversedge, S.P.; Drieghe, D.; Li, X.; Yan, G.; Bai, X.; Hyönä, J. Universality in eye movements and reading: A trilingual investigation. Cognition 2016, 147, 1–20. [Google Scholar] [CrossRef] [Green Version]
- Buscher, G.; Dengel, A.; Biedert, R.; Van Elst, L. Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond. ACM Trans. Interact. Intell. Syst. 2012, 1, 1–30. [Google Scholar] [CrossRef]
- Steichen, B.; Carenini, G.; Conati, C. Adaptive Information Visualization-Predicting user characteristics and task context from eye gaze. In Proceedings of the International Conference on User Modeling, UMAP Workshops, Montreal, QC, Canada, 16–20 July 2012; Volume 872. [Google Scholar]
- Loyola, P.; Brunetti, E.; Martinez, G.; Velásquez, J.D.; Maldonado, P. Leveraging Neurodata to Support Web User Behavior Analysis. In Wisdom Web of Things; Zhong, N., Ma, J., Liu, J., Huang, R., Tao, X., Eds.; Web Information Systems Engineering and Internet Technologies Book Series; Springer: Cham, Switzerland, 2016; pp. 181–207. [Google Scholar]
- Sharafi, Z.; Shaffer, T.; Sharif, B.; Guéhéneuc, Y.-G. Eye-Tracking Metrics in Software Engineering. In Proceedings of the 2015 Asia-Pacific Software Engineering Conference, New Delhi, India, 1–4 December 2015; IEEE Press: Piscataway, NJ, USA, 2015; pp. 96–103. [Google Scholar]
- Kovari, A.; Katona, J.; Costescu, C. Evaluation of Eye-Movement Metrics in a Software Debugging Task using GP3 Eye Tracker. Acta Polytech. Hung. 2020, 17, 57–76. [Google Scholar] [CrossRef]
- Katona, J.; Kovari, A.; Heldal, I.; Costescu, C.; Rosan, A.; Demeter, R.; Thill, S.; Stefanut, T. Using Eye- Tracking to Examine Query Syntax and Method Syntax Comprehension in LINQ. In Proceedings of the 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Stockholm, Sweden, 23–25 September 2020. [Google Scholar]
- Akuma, S.; Iqbal, R.; Jayne, C.; Doctor, F. Comparative analysis of relevance feedback methods based on two user studies. Comput. Hum. Behav. 2016, 60, 138–146. [Google Scholar] [CrossRef] [Green Version]
- Boi, P.; Fenu, G.; Spano, L.; Vargiu, V. Reconstructing User’s Attention on the Web through Mouse Movements and Perception-Based Content Identification. ACM Trans. Appl. Percept. 2016, 13, 1–21. [Google Scholar] [CrossRef] [Green Version]
- Zhao, Q.; Chang, S.; Harper, M.; Konstan, J.A. Gaze Prediction for Recommender Systems. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16), Association for Computing Machinery, New York, NY, USA, 15–19 September 2016; pp. 131–138. [Google Scholar]
- Ujbanyi, T.; Kovari, A.; Sziládi, G.; Katona, J. Examination of the eye-hand coordination related to computer mouse movement. Infocommun. J. 2020, 12, 26–31. [Google Scholar] [CrossRef]
- Sulikowski, P. Evaluation of Varying Visual Intensity and Position of a Recommendation in a Recommending Interface Towards Reducing Habituation and Improving Sales. In Advances in E-Business Engineering for Ubiquitous Computing, Proceedings of the 16th International Conference on E-Business Engineering, ICEBE 2019, Shanghai, China, 11–13 October 2019; Lecture Notes on Data Engineering and Communications Technologies; Chao, K.M., Jiang, L., Hussain, O., Ma, S.P., Fei, X., Eds.; Springer: Cham, Switzerland, 2020; Volume 41, pp. 208–218. [Google Scholar]
- Sulikowski, P.; Zdziebko, T. Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing. Electronics 2020, 9, 266. [Google Scholar] [CrossRef] [Green Version]
Star Rating | Number of Ratings |
---|---|
***** | 415 |
**** | 346 |
*** | 325 |
** | 180 |
* | 130 |
Store rc_layout | prod_recommended_time | rel_recommended_time_document_length | rel_recommended_time_recommended_length | rel_recommended_time_page_time | rel_recommended_time_user_active | rel_recommended_time_tab_active |
---|---|---|---|---|---|---|
Electro.pl vertical | 4029 | 0.157 | 2.048 | 0.089 | 0.174 | 0.096 |
Electro.pl horizontal | 1858 | 0.072 | 0.944 | 0.041 | 0.080 | 0.044 |
Agito.pl vertical | 2669 | 0.075 | 1.584 | 0.059 | 0.101 | 0.067 |
Agito.pl horizontal | 1920 | 0.054 | 1.304 | 0.043 | 0.073 | 0.048 |
Morele.net vertical | 1777 | 0.104 | 0.905 | 0.054 | 0.079 | 0.052 |
Morele.net horizontal | 1953 | 0.114 | 0.752 | 0.059 | 0.087 | 0.058 |
Merlin.pl vertical | 1977 | 0.108 | 0.351 | 0.049 | 0.156 | 0.084 |
Merlin.pl horizontal | 1312 | 0.072 | 0.285 | 0.032 | 0.104 | 0.056 |
Store | Registered prod_recommended_time | Registered prod_review_time |
---|---|---|
Agito.pl | 38 | 31 |
Electro.pl | 52 | 31 |
Komputronik.pl | 39 | 24 |
Merlin.pl | 236 | 66 |
Morele.net | 58 | 15 |
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Sulikowski, P.; Zdziebko, T. Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking. Appl. Sci. 2021, 11, 56. https://doi.org/10.3390/app11010056
Sulikowski P, Zdziebko T. Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking. Applied Sciences. 2021; 11(1):56. https://doi.org/10.3390/app11010056
Chicago/Turabian StyleSulikowski, Piotr, and Tomasz Zdziebko. 2021. "Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking" Applied Sciences 11, no. 1: 56. https://doi.org/10.3390/app11010056
APA StyleSulikowski, P., & Zdziebko, T. (2021). Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking. Applied Sciences, 11(1), 56. https://doi.org/10.3390/app11010056