Online Product Recommendation System by Using Eye Gaze Data

Shahriar, Ahanaf; Moon, Mahedee Zaman; Mahmud, Hasan; Hasan, K. M. Azharul

doi:10.1145/3377049.3377108

Cited by 12 publications

(23 citation statements)

References 10 publications

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“…Numerous parameters related to the recommendation interfaces include the times the cursor is located in recommendation areas, their physical size, and registered product interest. As shown by other studies the motion of the mouse cursor is correlated with eye motion and, thus, user interest [18,[40][41][42], and a number of behavior-related parameters can be deduced, similar to previous studies [43,44]. It has been shown that mouse and keystroke tracking can definitely be used as a lower-quality yet even-less-intrusive alternative to gaze-tracking which can only be performed during a controlled study requiring additional equipment, its calibration, and supervision of a research worker.…”

Section: Assumptions and Methodologymentioning

confidence: 64%

“…One can seek the most appropriate ways to make recommendations to users by observing human-computer interactions with recommending interfaces. People's behavior while interacting with webpages can be tracked by registering user's generated events inside web browsers [3] or by using gaze tracking solutions, and counting the number of times the user moved over or browsed a given element of the website, in order to learn user's preferences and generate a set of similar products constituting the basis for recommendations [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking

Sulikowski

Zdziebko

2020

Applied Sciences

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Recommender systems play a vital role in e-commerce by increasing the likelihood of transactions and improving sales thanks to presenting personal recommendations. Due to the marketing habituation effect, users are less and less responsive to this type of content. Visual recommendation presentation, in particular the recommendation zone layout can influence the effectiveness of a recommendation. This study examines human–computer interactions for vertical, horizonal, and mixed layouts of recommending interfaces of four major e-commerce stores, and is based on our document object model events-based behavior analysis tool. Results from this implicit feedback study are presented and analyzed, showing that vertical recommendation zones attracted more attention than horizontal ones.

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Section: Assumptions and Methodologymentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking

Sulikowski

Zdziebko

2020

Applied Sciences

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“…These are rectangle areas around the elements for which relevance is elicited. An AOI may encompass a static graphic element such as an image [5,6,8,14,15,26,27,29,32,36,39,41,46] or interaction element [21]. In text documents, it usually holds a single word [12,13,18,35,38,46].…”

Section: What Gaze Features Contain Relevance Information?mentioning

confidence: 99%

“…The relevance judgment is thus based on a comparison between multiple elements. The most detailed models quantify the relevance that each object has to the user [5,6,8,12,27,29,46]. This allows to rank the elements.…”

Section: How Is Relevance Defined?mentioning

confidence: 99%

Is This Really Relevant? A Guide to Best Practice Gaze-based Relevance Prediction Research

Heck¹,

Sonntag

Becker

2021

Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

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As eye tracking is becoming feasible on commodity devices, it provides a powerful tool for inferring users' perceived relevance of objects. Yet the prediction quality depends on multiple parameters that have to be considered when designing the prediction model. In this paper, we review approaches to predict relevance from gaze with regard to five design issues: 1) extracting features, 2) defining the algorithm, 3) setting a prediction scope, 4) eliminating visual distractors, and 5) evaluating the system. The insights may serve as a guide to establish best practices for the design and evaluation of relevance prediction models, thus allowing for better comparability of future work. We further discuss promising fields of application that will drive future research on gaze-based relevance prediction. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Interaction design process and methods; Contextual design.

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“…Recently, the use of deep neural networks has grown in interest among recommender system researchers [8]. Shahriar et al (2020) used eye tracking to learn user behavior by counting how many times a user looked at a particular website element in order to generate a cluster of similar products to constitute a base for recommendations [9].…”

Section: Introductionmentioning

confidence: 99%

Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

Sulikowski

Zdziebko

Coussement

et al. 2021

Sensors

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Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon.

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Online Product Recommendation System by Using Eye Gaze Data

Cited by 12 publications

References 10 publications

Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking

Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking

Is This Really Relevant? A Guide to Best Practice Gaze-based Relevance Prediction Research

Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase

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