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Featured Application: The proposed e-commerce customer preference monitoring (ECPM) tool can be utilized for any website, but especially e-commerce websites, for collecting a stream of user-activity data, which could be used to adapt an interface to fit user needs in real time. Besides personalizing a website interface, observing user activity can provide information about their preferences, which can be used to dynamically individualize different type of content presented to user, e.g., recommendations of products. Observing user interactions with computer interfaces can lead to better understanding of user needs and, thus, build an intelligent recommender system which will understand and guide each user through the cognitive process of discovering products that would meet individual preferences. 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. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate how a study group consisting of 85 participants interact with selected online stores and how the interactions correlate with interest in products for each store. This work uses a quantitative research methodology involving a dedicated tool for implicit monitoring of human-website interaction, instrumented for selected stores and registering product interest for the benefit of recommender systems. One of the findings was that to predict product interest it seems a good idea to start with monitoring scrolling activities, mouse usage and time spent on a website and its sections. Rich product information played a crucial role in shaping user interest. For all stores in the study, a misclassification rate of 28.7% was achieved in a CART model, while modeling for particular stores, it varied from 39.3% to 24.8%, and we feel that it reflected different page layouts in stores. Models built to represent individual behavior patterns of most active study participants varied in terms of misclassification rates from 17% to 26%, and the analysis suggested that individual preference modeling could be considered for recommender systems, in particular for key customers or customer groups. The study leads to insights into online store user behavior and product interest prediction, and as a result to possible implications for recommender systems design, including ergonomics optimization and interaction personalization. [ABSTRACT FROM AUTHOR]

This paper aims to present full results of an e-comerce customer preferences identification study using classification trees. The study led to building classification models based on indicators obtained using implicit feedback. The paper constitutes a starting point for further research in the field of recommender systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The increasing amount of marketing content in e-commerce websites results in the limited attention of users. For recommender systems, the way recommended items are presented becomes as important as the underlying algorithms for product selection. In order to improve the effectiveness of content presentation, marketing experts experiment with the layout and other visual aspects of website elements to find the most suitable solution. This study investigates those aspects for a recommending interface. We propose a framework for performance evaluation of a recommending interface, which takes into consideration individual user characteristics and goals. At the heart of the proposed solution is a deep neutral network trained to predict the efficiency a particular recommendation presented in a selected position and with a chosen degree of intensity. The proposed Performance Evaluation of a Recommending Interface (PERI) framework can be used to automate an optimal recommending interface adjustment according to the characteristics of the user and their goals. The experimental results from the study are based on research-grade measurement electronics equipment Gazepoint GP3 eye-tracker data, together with synthetic data that were used to perform pre-assessment training of the neural network. [ABSTRACT FROM AUTHOR]

Summary: This paper presents use of a tool built to monitor human‐website interaction without the need for eye tracking. Behavior indicators calculated from data collected by the tool can be used for many purposes such as web ergonomics enhancement, content adaptation, and, in particular, recommender systems. A random‐forests‐based modeling approach is shown as a generalization for earlier decision‐tree classification approach. Results of a usability survey conducted within the study are presented. [ABSTRACT FROM AUTHOR]