Adam Wierzbicki

Equitable aggregations and multiple criteria analysis

Abstract In the past decade, increasing interest in equity issues resulted in new methodologies in the area of operations research. This paper deals with the concept of equitably efficient solutions to multiple criteria optimization problems. Multiple criteria optimization usually starts with an assumption that the criteria are incomparable. However, many applications arise from situations which present equitable criteria. Moreover, some aggregations of criteria are often applied to select efficient solutions in multiple criteria analysis. The latter enforces comparability of criteria (possibly rescaled). This paper presents aggregations which can be used to derive equitably efficient solutions to both linear and nonlinear multiple optimization problems. An example with equitable solutions to a capital budgeting problem is analyzed in detail. An equitable form of the reference point method is introduced and analyzed.

Enriching Trust Prediction Model in Social Network with User Rating Similarity

Trust management is an increasingly important issue in large social networks, where the amount of data is too extensive to be analysed by ordinary users. Hence there is an urgent need for research aiming at building automated systems that can support users in making their decisions concerning trust.This work is a preliminary implementation of selected ideas described in our previous research proposal which concerns taking a machine-learning approach to the problem of trust prediction in social networks.We report experiments conducted on a publicly available social network dataset epinions.com. The results indicate that i) it is possible to predict trust to some extent, but much room for improvement is present; ii) enriching the model with attributes based on similarity between users can significantly improve trust prediction accuracy for more similar users.

Trust and Fairness in Open, Distributed Systems

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

The sound of silence: mining implicit feedbacks to compute reputation

A reliable mechanism for scoring the reputation of sellers is crucial for the development of a successful environment for customer-to-customer e-commerce. Unfortunately, most C2C environments utilize simple feedback-based reputation systems, that not only do not offer sufficient protection from fraud, but tend to overestimate the reputation of sellers by introducing a strong bias toward maximizing the volume of sales at the expense of the quality of service. In this paper we present a method that avoids the unfavorable phenomenon of overestimating the reputation of sellers by using implicit feedbacks. We introduce the notion of an implicit feedback and we propose two strategies for discovering implicit feedbacks. We perform a twofold evaluation of our proposal. To demonstrate the existence of the implicit feedback and to propose an advanced method of implicit feedback discovery we conduct experiments on a large volume of real-world data acquired from an online auction site. Next, a game-theoretic approach is presented that uses simulation to show that the use of the implicit feedback can improve a simple reputation system such as used by eBay. Both the results of the simulation and the results of experiments prove the validity and importance of using implicit feedbacks in reputation scoring.

Learning About the Quality of Teamwork from Wikiteams

This paper describes an approach to evaluating teams of contributors in Wikipedia based on social network analysis. We present the idea of creating an implicit social network based on characteristics of pages’ edit history and collaboration between contributors. This network consists of four dimensions: trust, distrust, acquaintance and knowledge. Trust and distrust are based on content modifications (copying and deleting respectively), acquaintance is based on the amount of discussion on articles’ talk pages between a given pair of authors and knowledge is based on the categories in which an author typically contributes. Our social network is based on the entire Wikipedia edit history, and therefore is a summary of all recorded author interactions. This social network can be used to assess the quality of a team of authors and consequently, to recommend good teams. The social network can also be used by Wikipedia authors and editors as an additional tool that allows to improve the author’s collaboration, as it expresses each author’s social environment and can be navigated to discover new projects that an author can participate in, or to recommend new collaborators.

WikiTeams: How Do They Achieve Success?

Web 2.0 technology and so-called social media are among the most popular (among users and researchers alike) Internet technologies today. Among them, Wiki technology - created to simplify HTML editing and enable open, collaborative editing of pages by ordinary Web users - occupies an important place. Wiki is increasingly adopted by businesses as a useful form of knowledge management and sharing, creating “corporate Wikis.” However, the most widely known application of Wiki technology - Wikipedia - is, according to many analysts, more than just an open encyclopedia that uses Wiki.

GitHub Projects. Quality Analysis of Open-Source Software

Nowadays Open-Source Software is developed mostly by decentralized teams of developers cooperating on-line. GitHub portal is an online social network that supports development of software by virtual teams of programmers. Since there is no central mechanism that governs the process of team formation, it is interesting to investigate if there are any significant correlations between project quality and the characteristics of the team members. However, for such analysis to be possible, we need good metrics of a project quality. This paper develops two such metrics, first one reflecting project’s popularity, and the second one - the quality of support offered by team members to users. The first metric is based on the number of ‘stars’ a project is given by other GitHub members, the second is obtained using survival analysis techniques applied to issues reported on the project by its users. After developing the metrics we have gathered characteristics of several GitHub projects and analyzed their influence on the project quality using statistical regression techniques.

Spiral of hatred: social effects in Internet auctions. Between informativity and emotion

An auction platform is a dynamic environment where a rich variety of social effects can be observed. Most of those effects remain unnoticed or even hidden to ordinary users. The in-depth studies of such effects should allow us to identify and understand the key factors influencing users’ behaviour. The material collected from the biggest Polish auction house has been analyzed. NLP algorithms were applied to extract sentiment-related content from collected comments and to measure informativity. Emotional distance between negative, neutral and positive comments has been calculated. The obtained results confirm the existence of the spiral-of-hatred effect but also indicate that much more complex patterns of mutual relations between sellers and buyers exist. The last section contains a several suggestions which can prove useful to improve trustworthiness of users’ reports and security of an auction platform in general.

Interdisciplinary Matchmaking: Choosing Collaborators by Skill, Acquaintance and Trust

Social networks are commonly used to enhance recommender systems. Most of such systems recommend a single resource or a person. However, complex problems or projects usually require a team of experts that must work together on a solution. Team recommendation is much more challenging, mostly because of the complex interpersonal relations between members. This chapter presents fundamental concepts on how to score a team based on members’ social context and their suitability for a particular project. We represent the social context of an individual as a three-dimensional social network (3DSN) composed of a knowledge dimension expressing skills, a trust dimension and an acquaintance dimension. Dimensions of a 3DSN are used to mathematically formalize the criteria for prediction of the team’s performance. We use these criteria to formulate the team recommendation problem as a multi-criteria optimization problem. We demonstrate our approach on empirical data crawled from two web2.0 sites: onephoto.net and a social networking site. We construct 3DSNs and analyze properties of team’s performance criteria.

Predicting Controversy of Wikipedia Articles Using the Article Feedback Tool

Different points of view, opinions and controversies constitute the inherent part of modern society. Early detection of controversy is crucial for increasing productivity in peer production systems. The paper presents novelty approach to detecting controversial articles on the Wikipedia based on users ratings from Article Feedback Tool. The performance of proposed approach is on par with state-of-the-art solutions, but may also be applied outside Wikipedia-like systems. Additionally, emotion polarity measures can be used to locate controversial parts of articles, based on talk pages sections. With help of proposed algorithms, all articles in English Wikipedia have been tagged as either controversial or non-controversial. The dataset has been published and can be used by other researchers.