Jarosław Jankowski

Fuzzy multi-objective modeling of effectiveness and user experience in online advertising

We analyze the impact of online advertisements on effectiveness and user experience.We combine results from online campaign and data from perceptual experiment.We use fuzzy multi-objective modeling for searching for trade-off solutions.We propose balanced approach to advertising resources exploitation. The focus placed on maximizing user engagement in online advertising negatively affects the user experience because of advertising clutter and increasing intrusiveness. An intelligent decision support system providing balance between user experience and profits from online advertising based on the fuzzy multi-objective optimization model is presented in this paper. The generalized mathematical model uses uncertain parameters for content descriptors that are difficult to be precisely defined and measured, such as the level of intrusiveness and the change in performance over time. The search for final decision solutions and the verification of the proposed model are based on experimental results from both perceptual studies, which are evaluating visibility and intrusiveness of marketing content as well as online campaigns providing interaction data for estimation of effectiveness. Surprisingly, the online response to the most noticeable advertisements with highly perceived visibility and intrusiveness was relatively low. During the field study performed in order to compute the model parameters, the best results were achieved for advertising content with moderate visual influence on web users. Simulations with the proposed model revealed that a growing level of persuasion can increase results only to a certain extent. Above a saturation point, a strategy based on extensive visual effects, such as high-frequency flashing, resulted in a very high increase of intrusiveness and a slightly better performance in terms of acquired interactions. Proposed balanced content design with the use of intelligent decision support system creates directions towards sustainable advertising and a friendlier online environment.

Towards the Tradeoff Between Online Marketing Resources Exploitation and the User Experience with the Use of Eye Tracking

Online systems are often overloaded with marketing content and as a result, perceived intrusiveness negatively affects the user experience and the evaluation of the website. Intentional and unintentional avoidance of the commercial content creates the need for compromise solutions from both the perspective of user experience and business goals. The presented research shows a unique approach to search for tradeoffs between the editorial content and the intensity of marketing components with the use of eye tracking and the multiple-criteria decision analysis methods.

Method of Criteria Selection and Weights Calculation in the Process of Web Projects Evaluation

The article outlines the issues of website quality assessment, reduction of website assessment criteria and factors that users employ in the assessment of websites. The research presents a selection procedure concerning significant choice criteria and revealing undisclosed user preferences based on the website quality assessment models. The formulated procedure utilizes feature selection methods derived from machine learning. Results concerning undisclosed preferences were verified through a comparison with those declared by website users.

The multidimensional study of viral campaigns as branching processes

Viral campaigns on the Internet may follow variety of models, depending on the content, incentives, personal attitudes of sender and recipient to the content and other factors. Due to the fact that the knowledge of the campaign specifics is essential for the campaign managers, researchers are constantly evaluating models and real-world data. The goal of this article is to present the new knowledge obtained from studying two viral campaigns that took place in a virtual world which followed the branching process. The results show that it is possible to reduce the time needed to estimate the model parameters of the campaign and, moreover, some important aspects of time-generations relationship are presented.

Balancing Speed and Coverage by Sequential Seeding in Complex Networks

Information spreading in complex networks is often modeled as diffusing information with certain probability from nodes that possess it to their neighbors that do not. Information cascades are triggered when the activation of a set of initial nodes – seeds – results in diffusion to large number of nodes. Here, several novel approaches for seed initiation that replace the commonly used activation of all seeds at once with a sequence of initiation stages are introduced. Sequential strategies at later stages avoid seeding highly ranked nodes that are already activated by diffusion active between stages. The gain arises when a saved seed is allocated to a node difficult to reach via diffusion. Sequential seeding and a single stage approach are compared using various seed ranking methods and diffusion parameters on real complex networks. The experimental results indicate that, regardless of the seed ranking method used, sequential seeding strategies deliver better coverage than single stage seeding in about 90% of cases. Longer seeding sequences tend to activate more nodes but they also extend the duration of diffusion. Various variants of sequential seeding resolve the trade-off between the coverage and speed of diffusion differently.

Knowledge management in MCDA domain

Multi-criteria decision analysis (MCDA) methods have become increasingly popular in decision-making. Numerous methods in this field were developed to solve real-world decision problems including various engineering and scientific areas. Unfortunately, the proper use of each method is difficult due to the dispersion of the domain knowledge and lack of knowledge databases in this area. The paper presents research focused on knowledge management aspects in MCDA domain. In order to achieve a high level of practicality on different levels of decision making, the ontology as a form of conceptualization is implemented.

The Selection of Multicriteria Method Based on Unstructured Decision Problem Description

Decision support processes and methods require applying numerous mathematical transformations, including one of the developed processes of multicriteria analysis. The core of most existing processes is usually one of the multicriteria decision aid methods (MCDA). The paper presents research focused on identifying which factors of a decision situation are significant for selecting a multicriteria method. The identified factors were analyzed with data-mining methods. Conclusions contain an outline of factors of decision situations that support MCDA methods to support decisions in particular situations.

Knowledge Management in Website Quality Evaluation Domain

This paper deals with the problem of building a repository of knowledge about the methods and models of assessing the quality of Internet services. The repository has been constructed in the form of ontologies representing the various methods of quality assessment. For this purpose, the algorithm was developed based on ontologies with conceptualization of knowledge contained in the individual methods, and each completed ontology was evaluated. As a result of the research, domain ontologies were implemented reflecting the website quality assessment methods.

Balanced Approach to the Design of Conversion Oriented Websites with Limited Negative Impact on the Users

Web design requires consideration of many factors related to the usability, user experience and business objectives. In many cases conflict of interest can be observed and the desire can arise to improve the results represented by the conversions even at the expense of the user experience. This paper proposes a balanced approach which provides the ability to improve effects with a limited negative impact on users using interactive objects with adjustable levels of persuasion.

Guideline for MCDA Method Selection in Production Management Area

Each decision situation is described by a set of certain characteristics—a factor which classifies it into a category of decision problems. Many times the chosen problematic determines the choice of the most suitable Multi-Criteria Decision Analysis (MCDA) method to be employed for supporting a decision maker. The following paper deals with directions developing in the literature on choosing the MCDA method best suited to solve a given decision problem. In the study, two sources of factors which influence the choice of the method were identified: a subject of the decision and a characteristic of dependencies in the problem description. When considering factors originating from the subject of the decision, the main focus is on case studies which support applying a particular method to a given problem. Dependencies between parameters describing a problem were analysed from the impact that the existence of various data sources has to them. The selected group of factors was consecutively generalised and its impact on the result of the decision support (using a collection of methods) was pointed out. The performed analysis constitutes the source of the strategy for choosing one method from the considered group of methods. Examples of applications in production management area are given.