John O'Donovan

• PhD in Computer Science, MS in Computer Science, BS in Computer Science
• Research Associate at University of California, Santa Barbara

Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating and visualizing personalized explanations for recommender systems that incorporate signals from man...

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs fr...

Augmented reality (AR) interfaces increasingly utilize artificial intelligence systems to tailor content and experiences to the user. We explore the effects of one such system — a recommender system for online shopping — which allows customers to view personalized product recommendations in the physical spaces where they might be used. We describe...

With the spread of false and misleading information in current news, many algorithmic tools have been introduced with the aim of assessing bias and reliability in written content. However, there has been little work exploring how effective these tools are at changing human perceptions of content. To this end, we conduct a study with 654 participant...

Many network scientists have investigated the problem of mitigating or removing false information propagated in social networks. False information falls into two broad categories: disinformation and misinformation. Disinformation represents false information that is knowingly shared and distributed with malicious intent. Misinformation in contrast...

We investigated human understanding of different network visualizations in a large-scale online experiment. Three types of network visualizations were examined: node-link and two different sorting variants of matrix representations on a representative social network of either 20 or 50 nodes. Understanding of the network was quantified using task ti...

With the spread of false and misleading information in current news, many algorithmic tools have been introduced with the aim of assessing bias and reliability in written content. However, there has been little work exploring how effective these tools are at changing human perceptions of content. To this end, we conduct a study with 654 participant...

Decision support systems (DSS), which are often based on complex statistical, machine learning, and AI models, have increasingly become a core part of data analytics and sensemaking processes. Automation complacency -- a state characterized by over-trust in intelligent systems -- has the potential to result in catastrophic performance failure. An u...

Recommender systems have become pervasive on the web, shaping the way users see information and thus the decisions they make. As these systems get more complex, there is a growing need for transparency. In this paper, we study the problem of generating and visualizing personalized explanations for hybrid recommender systems, which incorporate many...

Intelligent assistants, such as navigation, recommender, and expert systems, are most helpful in situations where users lack domain knowledge. Despite this, recent research in cognitive psychology has revealed that lower-skilled individuals may maintain a sense of illusory superiority, which might suggest that users with the highest need for advice...

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs fr...

We conducted a fundamental user study to assess potential benefits of AR technology for immersive vocabulary learning. With the idea that AR systems will soon be able to label real-world objects in any language in real time, our within-subjects (N=52) lab-based study explores the effect of such an AR vocabulary prompter on participants learning nou...

Recommender systems are evaluated based on both their ability to create a satisfying user experience and their ability to help a user make better choices. Despite this, quantitative evidence from previous research in recommender systems indicate very high correlations between user experience attitudes and choice satisfaction. This might imply inval...

A large amount of research in recommender systems focuses on algorithmic accuracy and optimization of ranking metrics. However, recent work has unveiled the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this paper introduces MoodPlay...

User interfaces that display dynamic information have the ability to influence decision makers in networked settings where many individuals collaborate. To understand how varying levels of information support affects behavior (cooperation vs. defection) in a social dilemma, a user interface (UI) was developed and an online experiment (N=901) was co...

This paper describes "ARbis Pictus" --a novel system for immersive language learning through dynamic labeling of real-world objects in augmented reality. We describe a within-subjects lab-based study (N=52) that explores the effect of our system on participants learning nouns in an unfamiliar foreign language, compared to a traditional flashcard-ba...

Hybrid recommender systems combine several different sources of information to generate recommendations. These systems demonstrate improved accuracy compared to single-source recommendation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of ex...

As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selec...

In recent years the greater part of news dissemination has shifted from traditional news media to individual users on microblogs such as Twitter and Reddit. Therefore, there has been increasing research effort on how to automatically detect newsworthy and otherwise useful information on these platforms. In this paper, we present two novel algorithm...

In the cosmetics domain, many online sellers support user-provided product reviews. It has been shown that reviews have a profound effect on product conversion rates. Reviews of cosmetic products carry particular importance in purchasing decisions because of their personal nature, and particularly because of the potential for irritation with unsuit...

Awareness is a key user interface and interaction paradigm. Choosing what to make the user aware of, at what time, and how, has a critical impact on system usage and overall perception. In this workshop, we will bring together those from academia and industry to share their own work in this area, debate key topics, and brainstorm possible future co...

Bias is a common problem in today's media, appearing frequently in text and in visual imagery. Users on social media websites such as Twitter need better methods for identifying bias. Additionally, activists --those who are motivated to effect change related to some topic, need better methods to identify and counteract bias that is contrary to thei...

Bias is a common problem in today's media, appearing frequently in text and in visual imagery. Users on social media websites such as Twitter need better methods for identifying bias. Additionally, activists --those who are motivated to effect change related to some topic, need better methods to identify and counteract bias that is contrary to thei...

Many interactive systems in today's world can be viewed as providing advice to their users. Commercial examples include recommender systems, satellite navigation systems, intelligent personal assistants on smartphones, and automated checkout systems in supermarkets. We will call these systems that support people in making choices and decisions arti...

In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce “HopTopics” – a novel interactive tool for exploring content that is popular just beyond a user’s typical information...

Windshield displays (WSDs) are the big siblings of Head-up displays (HUDs). They are assumed to cover the entire windshield and to allow displaying content at continuous depth, eventually. This creates a large and unstructured 3D space for information display -- raising the question what to display where. To address this question, we developed a vi...

As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selec...

A large body of research in recommender systems focuses on optimizing prediction and ranking. However, recent work has highlighted the importance of other aspects of the recommendations, including transparency, control and user experience in general. Building on these aspects, we introduce MoodPlay, a hybrid recommender system music which integrate...

As co-chairs of the 21st ACM International Conference on Intelligent User Interfaces (ACM IUI 2016), we are pleased to share with the SIGAI community our thoughts and experiences with this year's edition of the conference. This year's conference was held in Sonoma, California, USA (http://iui.acm.org/2016/), a short drive to the north of San Franci...

Objective: We investigated how increases in task-relevant information affect human decision-making performance, situation awareness (SA), and trust in a simulated command-and-control (C2) environment. Background: Increased information is often associated with an improvement of SA and decision-making performance in networked organizations. Howeve...

This paper presents a formative evaluation of an interface for inspecting microblog content. This novel interface introduces filters by communities, and network structure, as well as ranking of tweets. It aims to improving content discovery, while maintaining content relevance and sense of user control. Participants in the US and the UK interacted...

In recent years a large portion of news dissemination has shifted from traditional outlets to individual users on platforms such as Twitter and Facebook. Accordingly, methods for detecting newsworthy and otherwise useful information on these platforms have received a lot of research attention. In this paper, we present a novel algorithm to automati...

This paper identifies and evaluates key factors that influence credibility perception in microblogs. Specifically, we report on a demographic survey (N=81) followed by a user experiment (N=102) in order to answer the following research questions: (1) What are the important cues that contribute to information being perceived as credible? and (2) To...

In many of today's online applications that facilitate data exploration, results from information filters such as recommender systems are displayed alongside traditional search tools. However, the effect of prediction algorithms on users who are performing open-ended data exploration tasks through a search interface is not well understood. This pap...

As an interactive intelligent system, recommender systems are developed to give predictions that match users preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from th...

The trust that humans place on recommendations is key to the success of recommender systems. The formation and decay of trust in recommendations is a dynamic process influenced by context, human preferences, accuracy of recommendations, and the interactions of these factors. This paper describes two psychological experiments (N=400) that evaluate t...

In this paper, we present a unique study of two successful methods for computing message reliability. The first method is based on machine learning and attempts to find a predictive model based on network features. This method is generally geared towards assessing credibility of messages and is able to generate high recall results. The second metho...

User interface (UI) composition and information presentation can impact human trust behavior. Trust is a complex concept studied by disciplines like psychology, sociology, economics, and computer science. Definitions of trust vary depending on the context, but are typically based on the core concept of “reliance on another person or entity”. Trust...

Availability of \big data" from the Social Web provides a unique opportunity for synergy between the computational and social sciences. On one hand, psychologists and social scientists have developed and established models of human competence, credibility, trust and skill over many years. Currently, much research is being conducted by computer scie...

To understand the processes involved in trust-based judgments in a computer-mediated multi-agent setting, a user interface (UI) was developed and an experiment was devised based on the Iterated Diner's Dilemma, a variation of the n-player Prisoner's Dilemma. Analysis of the experiment resulted in two major findings: (1) UI composition and informati...

The web has evolved in a scale free manner, with available information about different entities developing in different forms, different locations, and at massive scales. This paper addresses the cognitive limitations that information analysts typically experience as they approach the boundaries where automated analysis algorithms are sorely needed...

Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. These data can be used to better understand people (e.g., personality traits, perceptions, and preferences) and predict their behavior. As a result, a deeper understanding of users and their behavior can benefit a wide range of intelligent applications,...

Increased popularity of microblogs in recent years brings about a need for better mechanisms to extract credible or otherwise useful information from noisy and large data. While there are a great number of studies that introduce methods to find credible data, there is no accepted credibility benchmark. As a result, it is hard to compare different s...

Increased popularity of microblogs in recent years brings about a need for better mechanisms to extract credible or otherwise useful information from noisy and large data. While there are a great number of studies that introduce methods to find credible data, there is no accepted credibility benchmark. As a result, it is hard to compare different st...

This paper presents LinkedVis, an interactive visual recom-mender system that combines social and semantic knowledge to produce career recommendations based on the LinkedIn API. A collaborative (social) approach is employed to identify professionals with similar career paths and produce personalized recommendations of both companies and roles. To u...

This paper discusses and evaluates the impact of visualization and interaction strategies for extracting quality information from data in complex networks such as microblogs. Two different approaches to interactive visual representations of data are discussed: an interactive node-link graph and a novel approach where content is separated into inter...

This paper studies the relationship between trust and Situation Awareness (SA) in a 3-Player Iterated Diner's Dilemma game. We ran an experiment in which 24 participants each played against two computer opponents for six blocks of gameplay, with different opponent strategies in each block. Based on SA theory and design principles, we developed thre...

This paper describes a machine learning approach to evaluate the relationship between trust behavior and Situation Awareness (SA) in the context of a 3-player Iterated Diner's Dilemma game. Our experimental setup consisted of a set of 24 supervised studies in which participants played against computer opponents with different cooperation strategies...

Standard approaches of information retrieval are increasingly complemented by social search even when it comes to rational information needs. Twitter, as a popular source of real-time information, plays an important role in this respect, as both the follower-followee graph and the many relationships among users provide a rich set of information pie...

Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. This data can be used to better understand people, such as their personality traits, perceptions, and preferences, and predict their behavior. This deeper understanding of users and their behaviors can benefit a wide range of intelligent applications, s...

This paper presents an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources, such as Wikipedia, Facebook, and Twitter. The system employs hybrid techniques from traditional recommender system literature, in addition to a novel interactive interface which serves to explain the recom...

Users of social recommender systems may want to inspect and control how their social relationships influence the recommendations they receive, especially since recommendations of social recommenders are based on friends rather than anonymous "nearest neighbors". We performed an online user experiment (N=267) with a Facebook music recommender system...

Twitter is a major forum for rapid dissemination of user-provided content in real time. As such, a large proportion of the information it contains is not particularly relevant to many users and in fact is perceived as unwanted 'noise' by many. There has been increased research interest in predicting whether tweets are relevant, newsworthy or credib...

This paper presents and evaluates three computational models for recommending credible topic-specific information in Twitter. The first model focuses on credibility at the user level, harnessing various dynamics of information flow in the underlying social graph to compute a rating. The second model applies a content-based strategy to compute a fin...

We present TopicNets, a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. A range of visualization types and control mechanisms to support knowledge discovery are presented. These include corpus- and document-specific views, iterative topic modeling, search, and visual filtering. Drill-d...

This paper describes TopicLens, an interactive tool for exploring and recommending items within large corpora, based on both so-cial metadata and topical associations. The system uses a hybrid visualization model that represents topics and content items side by side, allowing the user to actively explore recommendations rather than passively viewin...

Wikipedia is emerging as the dominant global knowledge repository. Recently, large numbers of users have collaborated to produce more structured information in the so called "info boxes''. However, editing this data requires even more care than editing standard wikitext, as one must follow arcane template syntax. This paper describes WiGipedia, a n...

At the heart of both social and semantic web paradigms is the support for any user to become an information provider. While this has huge benefits in terms of the scope of information available, it raises two important problems: firstly, the well researched problem of information overload, and secondly, the problem of assigning trustworthiness to a p...

We present SmallWorlds, a visual interactive graph-based interface that allows users to specify, refine and build item-preference profiles in a variety of domains. The interface facilitates expressions of taste through simple graph interactions and these preferences are used to compute personalized, fully transparent item recommendations for a targ...

Collaborative or "social" filtering has been successfully deployed over the years as a technique for analyzing large amounts of user-preference knowledge to predict interesting items for an individual user. The black-box nature of most collaborative filtering (CF) applications leave the user wondering how the system arrived at its recommendation. I...

Traditional network visualization tools inherently suffer from scalability problems, particularly when such tools are interactive and web-based. In this paper we introduce WiGis –Web-based Interactive Graph Visualizations. WiGis exemplify a fully web-based framework for visualizing large-scale graphs natively in a user’s browser at interactive fram...

The Social Web constitutes a shift in information flow from the traditional Web. Previously, content was provided by the owners of a website, for consumption by the end-user. Nowadays, these websites are being replaced by Social Web applications which are frameworks for the publication of user-provided content. Traditionally, Web content could be ‘...

ABSTRACT Collaborative ltering (CF) has been successfully de- ployed over the years to compute predictions on items based on a user’s correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommenda- tion. This note introduces PeerChooser, a collaborative recommender,sys...

Buyers and sellers in online auctions are faced with the task of deciding who to entrust their business to based on a very limited amount of information. Current trust ratings on eBay average over 99% positive (13) and are presented as a single num- ber on a user's profile. This paper presents a sys- tem capable of extracting valuable negative info...

Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users'...

The amount of business taking place in online marketplaces such as eBay is growing rapidly. At the end of 2005 eBay Inc. reported annual growth rates of 42.5% (3) and in February 2006 received 3 million user feedback com- ments per day (1). Now we are faced with the task of using the limited information provided on auction sites to transact with co...

1. ABSTRACT Systems that adapt to input from users are susceptible to attacks from those same users. Recommender systems are common targets for such attacks since there are finan- cial, political and many other motivations for influencing the promotion or demotion of recommendable items (2). Recent research has shown that incorporating trust and re...

Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations...

To be successful recommender systems must gain the trust of users. To do this they must demonstrate their ability to make reliable predictions. We ar- gue that collaborative filtering recommendation al- gorithms can benefit from explicit models of trust to inform their predictions. We present one such model of trust along with a cost-benefit analys...

Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's informa- tion space to suit their particular information needs. Col- laborative filtering is a successful and popular technique for producing recommendations based on similarities in use...

This paper proposes that there is a substantial relative difference in the performance of information-filtering algorithms as they are applied to different datasets, and that these performance differences can be leveraged to form the basis of an Adaptive Information Filtering System. We classify five different datasets based on metrics such as spar...

Information is becoming increasingly available in digital formats such as Web Pages, MP3 files and many others. This puts more emphasis on the need for reliable information filtering techniques. New recommendation algorithms are continuously being developed to deal with the problem of information overload. In this paper we present a new, regression...

Information filtering techniques are becoming more widely used as available information spaces grow exponentially larger. New techniques for filtering information are being developed to tackle the information overload problem. This paper presents an assessment of the perfor- mance of three popular recommendation stratagem over a range of diverse da...

ECENT advances in the field of microscopy and bio- imaging have brought about the need for better digital storage of image and meta data. Researchers are creating large amounts of image and meta-data that are very hard to search, organize, process and analyze. To aggravate this situation different manufacturers generate data in different and incomp...