scholarly journals Fairness in rankings and recommendations: an overview

2021 ◽  
Author(s):  
Evaggelia Pitoura ◽  
Kostas Stefanidis ◽  
Georgia Koutrika
Keyword(s):  
Recommender Systems ◽  
Search Engines ◽  
Data Driven ◽  
Sources Of Information ◽  
Future Work

AbstractWe increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems among others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this work, we aim at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are threefold: (a) to provide a solid framework on a novel, quickly evolving and impactful domain, (b) to present related methods and put them into perspective and (c) to highlight open challenges and research paths for future work.

scholarly journals COVID-19 Brings Data Equity Challenges to the Fore

2021 ◽  
Author(s):  
H.V. Jagadish ◽  
Julia Stoyanovich ◽  
Bill Howe
Keyword(s):  
Quality Control ◽  
Government Policy ◽  
Data Driven ◽  
Contact Tracing ◽  
Control Mechanisms ◽  
Sources Of Information ◽  
Decision Systems ◽  
Physical Resources ◽  
Multiple Levels ◽  
Data Driven Decisions

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: they can inform local, state and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise, and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.

Information seeking behaviour ofArab social science and humanities postgraduates: Adescriptive study

2021 ◽  
pp. 116-137
Author(s):  
Ahmed Maher Khafaga Shehata ◽  
Amr Hassan Fatouh Hassan
Keyword(s):  
Social Science ◽  
Search Engines ◽  
Information Seeking ◽  
English Language ◽  
Arabic Language ◽  
Information Seeking Behavior ◽  
Sources Of Information ◽  
Postgraduate Students ◽  
Social Sciences And Humanities ◽  
Seeking Behavior

The purpose of this paper is to report the findings of a study of information-seeking behavior among a group of Arab postgraduate students in social science and humanities disciplines. The paper also explores information-seeking styles and examines how information seeking is affected by external factors. The study employed a qualitative approach to explore informationseeking behavior in the sample and the sources of information used to obtain scholarly information. A sample of 33 participants was interviewed to elucidate the information-seeking behavior of the Arabic language speakers. The analysis of the interviews revealed that the participants use different methods to find information on the internet. These methods vary from using search engines to using sites that provide pirated scholarly papers. The data showed that most of the sample students use search engines and databases provided by their universities, but they should be trained in research ethics to avoid unacceptable research practices. The results also indicate that searching in other languages represents a challenge for Arab postgraduates in the social sciences and humanities. This study was conducted with social science and humanities postgraduates as part of a series of studies aiming to explore Arab language speakers' scholarly practices. The information-seeking behavior of science disciplines may differ, as the teaching language is mainly in English. This study contributes to the field by expanding our understanding of how non-English language speakers seek scholarly information and what sources are used to obtain the scholarly papers.

scholarly journals The Impact of Criteria-based and Data-driven Sampling Approaches on the Heterogeneity and Interpretability of Posttraumatic Stress Symptom Networks

2020 ◽  
Author(s):  
Santiago Papini ◽  
Mikael Rubin ◽  
Michael J Telch ◽  
Jasper A. J. Smits
Keyword(s):  
Data Driven ◽  
Network Heterogeneity ◽  
Psychopathological Symptom ◽  
Highly Correlated ◽  
And Cluster Analysis ◽  
Sensitivity Specificity ◽  
Future Work ◽  
The Impact ◽  
Sample Heterogeneity ◽  
Source Of Error

Background. The application of psychopathological symptom networks requires reconciliation of the observed cross-sample heterogeneity. We leveraged the largest sample to be used in a PTSD network analysis (N = 28,594) to examine the impact of criteria-based and data-driven sampling approaches on the heterogeneity and interpretability of networks.Methods. Severity and diagnostic criteria identified four overlapping subsamples and cluster analysis identified three distinct data-derived profiles. Networks estimated on each subsample were compared to a respective benchmark network at the symptom-relation level by calculating sensitivity, specificity, correlation, and density of the edges. Negative edges were assessed for Berkson’s bias, a source of error that can be induced by threshold samples on severity.Results. Criteria-based networks showed reduced sensitivity, specificity, and density but edges remained highly correlated and a meaningfully higher proportion of negative edges was not observed relative to the benchmark network of all cases. Among the data-derived profile networks, the Low Severity network had the highest proportion of negative edges not present in the benchmark network of symptomatic cases. The High Severity profile also showed a higher proportion of negative edges, whereas the Medium Severity profile did not. Conclusion. Although networks showed differences, Berkson’s bias did not appear to be a meaningful source of systematic error. These results can guide expectations about the generalizability of symptom networks across samples that vary in their ranges of severity. Future work should continue to explore whether network heterogeneity is reflective of meaningful and interpretable differences in the symptom relations from which they are composed.

Enhancing Citizens' Participation via Recommender Systems

2020 ◽  
pp. 624-650
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

scholarly journals Recommendation as Collaboration in Web Search

AI Magazine ◽  
2011 ◽  
Vol 32 (3) ◽  
pp. 35-45 ◽  
Author(s):  
Barry Smyth ◽  
Jill Freyne ◽  
Maurice Coyle ◽  
Peter Briggs
Keyword(s):  
Recommender Systems ◽  
Search Engines ◽  
Web Search ◽  
Online Information ◽  
Information Discovery ◽  
Proactive Approach ◽  
Systems Research ◽  
Traditional Approaches ◽  
New Generation

Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation, with a more proactive approach to discovery that is informed by the users interests and preferences. To date recommender systems have been deployed within a variety of e-commerce domains, covering a range of products such as books, music, movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery however and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.

Could What You Don’t Know Hurt You? Information Asymmetry in Land Markets in Late Antiquity

2010 ◽  
pp. 181-194
Author(s):  
P.V. (Meylekh) Viswanath ◽  
Michael Szenberg
Keyword(s):  
Information Asymmetry ◽  
Late Antiquity ◽  
Trading Volume ◽  
Middle Eastern ◽  
Second Century ◽  
Reasonable Assumption ◽  
Sources Of Information ◽  
Future Work ◽  
Common Era ◽  
Roman Palestine

Jewish legal texts are important sources of information on Middle Eastern economies in the early centuries of the Common Era (ce). This article focuses on the Rashi’s explanation of seasonality and trading volume fluctuations of land prices, as discussed in tractate Bava Kamma of the Babylonian Talmud (Roman Palestine). Although the text itself was redacted in Babylonia, it is largely a commentary on the Mishnah, an earlier text redacted in Roman Palestine toward the end of the second-century ce. It may be argued that information asymmetry is a reasonable assumption in a firm, where the activities of the managers are not easily observable. It might even be reasonable in the case of land with multiple uses. The evidence of the Talmud indicates that information asymmetry can considerably depress the prices of assets and affect the liquidity of markets. This indicates the importance of attempts to reduce information asymmetry. Future work on market frictions in antiquity might consider other examples of information asymmetry in the agricultural economies discussed in the Babylonian and Jerusalem Talmuds.

The Role of Activity and Similarity in Rating and Social Behavior in Social Recommender Systems

2019 ◽  
Vol 28 (06) ◽  
pp. 1960004
Author(s):  
Carine Pierrette Mukamakuza ◽  
Dimitris Sacharidis ◽  
Hannes Werthner
Keyword(s):  
Social Behavior ◽  
Recommender Systems ◽  
User Behavior ◽  
The Other ◽  
Social Connections ◽  
Sources Of Information ◽  
High Similarity ◽  
Strong Activity ◽  
Social Recommender Systems ◽  
The Social

Social recommender systems exploit two sources of information for making recommendations, the historical rating behavior of users, and the social connections among them. The basic assumption is that if two users are friends, they are likely to share similar preferences. Many recommendation approaches are based on such correlations between the rating and the social behavior of users. However, there is little work in studying whether there actually exist such correlations and how strong they are. In our work, we look at the two views of user behavior, their social connections, and their history of ratings, and investigate two research questions. The first examines if strong activity in one view, e.g., having many friends, implies strong activity in the other view, e.g., having rated many items. The second investigates whether high similarity in one view, e.g., network similarity, implies high similarity in the other view, e.g., rating similarity. We employ various notions of activity and similarity, and identify those that appear to have the stronger impact. Specifically, to some degree, we find that rating behavior determines social behavior, and that the opposite relationship is weaker.

scholarly journals On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 11
Author(s):  
Eva Blanco-Mallo ◽  
Beatriz Remeseiro ◽  
Verónica Bolón-Canedo ◽  
Amparo Alonso-Betanzos
Keyword(s):  
Recommender Systems ◽  
Sources Of Information ◽  
Target Domain ◽  
Specific Knowledge ◽  
Collaborative Environment ◽  
Learning Techniques ◽  
The World ◽  
Feature Extractor ◽  
Convolutional Autoencoder ◽  
The Web

Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users from all over the world seek and share their opinions based on all types of products. Specifically, millions of images tagged with users’ tastes are available on the web. Therefore, the application of deep learning techniques to solve these types of tasks has become a key issue, and there is a growing interest in the use of images to solve them, particularly through feature extraction. This work explores the potential of using only images as sources of information for modeling users’ tastes and proposes a method to provide gastronomic recommendations based on them. To achieve this, we focus on the pre-processing and encoding of the images, proposing the use of a pre-trained convolutional autoencoder as feature extractor. We compare our method with the standard approach of using convolutional neural networks and study the effect of applying transfer learning, reflecting how it is better to use only the specific knowledge of the target domain in this case, even if fewer examples are available.

scholarly journals Towards Cognitive Recommender Systems

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 176
Author(s):  
Amin Beheshti ◽  
Shahpar Yakhchi ◽  
Salman Mousaeirad ◽  
Seyed Mohssen Ghafari ◽  
Srinivasa Reddy Goluguri ◽  
...  
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
User Preferences ◽  
Data Driven ◽  
New Approach ◽  
Domain Experts ◽  
Adaptive Mechanisms ◽  
New Type ◽  
Cognitive Activities ◽  
Time Aware

Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.

Multifractal Mice: Measuring Task Engagement and Readiness-to-hand via Hand Movement

2020 ◽  
Author(s):  
Daniel Bennett
Keyword(s):  
Hand Movement ◽  
Task Engagement ◽  
Computational Method ◽  
Data Driven ◽  
Prior Work ◽  
Wide Range ◽  
Input Signals ◽  
Data Driven Approach ◽  
Future Work ◽  
And Task

We introduce an unobtrusive, computational method for measuring readiness-to-hand and task-engagement during interaction."Readiness-to-hand" is an influential concept describing fluid, intuitive tool use, with attention on task rather than tool; it has longbeen significant in HCI research, most recently via metrics of tool-embodiment and immersion. We build on prior work in cognitivescience which relates readiness-to-hand and task engagement to multifractality: a measure of complexity in behaviour. We conduct areplication study (N=28), and two new experiments (N=44, N=30), which show that multifractality correlates with task-engagement and other features of readiness-to-hand overlooked in previous measures, including familiarity with task. This is the first evaluation of multifractal measures of behaviour in HCI. Since multifractality occurs in a wide range of behaviours and input signals, we support future work by sharing scripts and data (https://osf.io/2hm9u/), and introducing a new data-driven approach to parameter selection

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