John Riedl

John Riedl
University of Minnesota Twin Cities | UMN · Department of Computer Science and Engineering

About

128
Publications
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31,111
Citations

Publications

Publications (128)
Article
Online open collaboration efforts, such as Wikipedia articles and open source software development, often involve a large crowd with diverse experiences and interests. Diversity, on the one hand, facilitates the access to and integration of a wide variety of information; on the other hand, it may cause conflict and hurt group performance. Although...
Conference Paper
Deviant behavior in online social systems is a difficult problem to address. Consequences of deviance include driving off users and tarnishing the system's public image. We present an examination of these concepts in a popular online game, League of Legends. Using a large collection of game records and player-given feedback, we develop a metric, to...
Conference Paper
One of the challenges for recommender systems is that users struggle to accurately map their internal preferences to external measures of quality such as ratings. We study two methods for supporting the mapping process: (i) reminding the user of characteristics of items by providing personalized tags and (ii) relating rating decisions to prior rati...
Conference Paper
In this paper we address the problem of developing actionable quality models for Wikipedia, models whose features directly suggest strategies for improving the quality of a given article. We first survey the literature in order to understand the notion of article quality in the context of Wikipedia and existing approaches to automatically assess ar...
Article
Full-text available
High turnover and under contribution are problems in many online communities, threatening their ability to provide resources for members and even their existence. This article describes two approaches for increasing attachment to online communities inspired by social psychological theory. With identity-based attachment, members feel connected to th...
Article
Considering how to combine the best elements of conferences and journals.
Article
This editorial introduction explains the motivation and origin of the TiiS special issue on Highlights of the Decade in Interactive Intelligent Systems and shows how its five articles exemplify the types of research contribution that TiiS aims to encourage and publish.
Article
Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in rati...
Article
Hybrid recommender systems --- systems using multiple algorithms together to improve recommendation quality --- have been well-known for many years and have shown good performance in recent demonstrations such as the NetFlix Prize. Modern hybridization techniques, such as feature-weighted linear stacking, take advantage of the hypothesis that the r...
Article
This article introduces the tag genome, a data structure that extends the traditional tagging model to provide enhanced forms of user interaction. Just as a biological genome encodes an organism based on a sequence of genes, the tag genome encodes an item in an information space based on its relationship to a common set of tags. We present a machin...
Article
Full-text available
Online communities are increasingly important to organizations and the general public, but there is little theoretically based research on what makes some online communities more successful than others. In this article, we apply theory from the field of social psychology to understand how online communities develop member attachment, an important d...
Conference Paper
Wikipedia has become one of the primary encyclopaedic information repositories on the World Wide Web. It started in 2001 with a single edition in the English language and has since expanded to more than 20 million articles in 283 languages. Criss-crossing between the Wikipedias is an inter-language link network, connecting the articles of one editi...
Article
Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in col-laborative filtering recommender system...
Conference Paper
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Dedicated and productive members who actively contribute to community efforts are crucial to the success of online volunteer groups such as Wikipedia. What predicts member productivity? Do productive members stay longer? How does involvement in multiple projects affect member contribution to the community? In this paper, we analyze data from 648 Wi...
Article
Do social media contribute to or detract from the creation of social capital, the vital currency that forms the basis of enduring relationships?
Article
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People from different cultures vary in cognition, emo-tion, and behavior. We explore cultural differences in a tagging system. We developed a model of cul-tural differences and performed a controlled empirical study with American and Chinese subjects to investi-gate questions that arise from the model. American and Chinese subjects differed in many...
Conference Paper
Recommender systems research is being slowed by the difficulty of replicating and comparing research results. Published research uses various experimental methodologies and metrics that are difficult to compare. It also often fails to sufficiently document the details of proposed algorithms or the evaluations employed. Researchers waste time reimpl...
Conference Paper
Full-text available
Wikipedia has rapidly become an invaluable destination for millions of information-seeking users. However, media reports suggest an important challenge: only a small fraction of Wikipedia's legion of volunteer editors are female. In the current work, we present a scientific exploration of the gender imbalance in the English Wikipedia's population o...
Article
Role-playing gamers take on the behavior they think appropriate for the "body" they inhabit in a virtual environment.
Article
Tags, words or short phrases attached to items on the Internet, organize the social Web. Unlike expert-maintained taxonomies such as the Dewey Decimal Classification, tags evolve organically based on the collective action of individual users. The collection of tags in a tagging system, called a folksonomy, can vary widely from site to site. The pap...
Article
Wikipedia faces real challenges in recruiting new editors and in keeping existing contributors productive.
Conference Paper
Full-text available
Tags help users understand a rich information space, by showing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key characteristics. For example, a user who loves Pulp Fiction m...
Article
Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their perfor...
Conference Paper
Online social production communities allow efficient construction of valuable and high-quality information sources. To be successful, community members must be effective at collaboration, including makink collective decisions in the presence of disagreement. We examined over 100,000 decisions made by small working groups in Wikipedia, and analyzed...
Conference Paper
Full-text available
In this paper we introduce tag expression, a novel form of preference elicitation that combines elements from tagging and rating systems. Tag expression enables users to apply affect to tags to indicate whether the tag describes a reason they like, dislike, or are neutral about a particular item. We present a user interface for applying affect to t...
Conference Paper
Full-text available
All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness i...
Conference Paper
The "wisdom of crowds" argument emphasizes the importance of diversity in online collaborations, such as open source projects and Wikipedia. However, decades of research on diversity in offline work groups have painted an inconclusive picture. On the one hand, the broader range of insights from a diverse group can lead to improved outcomes. On the...
Article
Full-text available
An online community is not sustainable unless at least a core of members participates and makes repeated visits. This article describes strategies for increasing commitment to online communities through two mechanisms inspired by social psychological theory – identity-based commitment, in which members feel connected to the group as a whole and its...
Conference Paper
Wikipedia has millions of articles, many of which receive little at- tention. One group of Wikipedians believes these obscure entries should be removed because they are uninteresting and neglected; these are the deletionists. Other Wikipedians disagree, arguing that this long tail of articles is precisely Wikipedia's advanta ge over other encyclope...
Conference Paper
Full-text available
Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collec- tions of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the flexibility and conceptual com...
Conference Paper
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While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from im- proving user satisfaction to helping users make better deci- sions. This paper introduces tagsplanations, which are ex- planations based on community tags. Tagsplanations have...
Conference Paper
Full-text available
Many websites use tags as a mechanism for improving item metadata through collective user e!ort. Users of tagging systems often apply far more tags to an item than a system can display. These tags can range in quality from tags that capture a key facet of an item, to those that are subjective, irrelevant, or misleading. In this paper we explore tag...
Article
Full-text available
Recommender systems are an effective tool to help find items of interest from an overwhelming number of available items. Collaborative Filtering (CF), the best known technology for recommender systems, is based on the idea that a set of like-minded users can help each other find useful information. A new user poses a challenge to CF recommenders, s...
Conference Paper
Full-text available
Collaborative Filtering (CF)-based recommender systems bring mutual benefits to both users and the operators of the sites with too much information. Users benefit as they are able to find items of interest from an unmanageable number of available items. On the other hand, e-commerce sites that employ recommender systems can increase sales revenue i...
Conference Paper
Rapid and continuous growth of digital libraries, coupled with brisk advancements in technology, has driven users to seek tools and services that are not only customized to their specific needs, but are also helpful in keeping them stay abreast with the latest developments in their field. TechLens is a recommender system that learns about its users...
Chapter
We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the p...
Conference Paper
Full-text available
Suppose you have a passion for items of a certain t ype, and you wish to start a recommender system around those items. You want a system like Amazon or Epinions, but for cookie recipes, local theater, or microbrew beer. How can you set up your recommender system without assembling complicated algorithms, large software infrastructu re, a large com...
Conference Paper
Full-text available
Many online communities use tags - community selected words or phrases - to help people find what they desire. The quality of tags varies widely, from tags that capture a key dimension of an entity to those that are profane, useless, or unintelligible. Tagging systems must often select a sub- set of available tags to display to users due to limited...
Conference Paper
Full-text available
Many small online communities would benefit from in- creased diversity or activity in their membership. Some communities run the risk of dying out due to lack of par- ticipation. Others struggle to achieve the critical mass nec- essary for diverse and engaging conversation. But what tools are available to these communities to increase partici- pati...
Conference Paper
Full-text available
Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community- maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said com- munities don't help members find work to do, or do so with...
Article
Wikipedia's brilliance and curse is that any user can edit any of the encyclopedia entries. We introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, we show that an overwhelming majority of the viewed words were written by freq...
Article
Full-text available
Collaborative Filtering (CF)-based recommender systems are indispensable tools to find items of interest from the unmanageable number of available items. Moreover, com-panies who deploy a CF-based recommender system may be able to increase revenue by drawing customers' attention to items that they are likely to buy. However, the sheer num-ber of cu...
Conference Paper
Full-text available
Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommenda...
Conference Paper
Full-text available
Recommender systems do no t always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender alg...
Conference Paper
Full-text available
Many online communities are emerging that, like Wikipedia, bring people together to build community-maintained artifacts of lasting value (CALVs). Motivating people to contribute is a key problem because the quantity and quality of contributions ultimately determine a CALV's value. We pose two related research questions: 1) How does intelligent tas...
Conference Paper
Full-text available
A tagging community's vocabulary of tags forms the basis for social navigation and shared expression. We present a user-centric model of vocabulary evolution in tagging com- munities based on community influence and personal ten- dency. We evaluate our model in an emergent tagging sys- tem by introducing tagging features into the MovieLens rec- omm...
Conference Paper
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In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, i...
Conference Paper
Full-text available
Item-oriented Web sites maintain repositories of informati- on about things such as books, games, or products. Many of these Web sites offer discussion forums. However, these forums are often disconnected from the rich data available in the item repositories. We describe a system, movie linking, that bridges a movie recommendation Web site and a mo...
Conference Paper
Recommender systems are widely used to help deal with the problem of information overload. However, recommenders raise serious privacy and security issues. The personal information collected by recom- menders raises the risk of unwanted exposure of that information. Also, malicious users can bias or sabotage the recommendations that are pro- vided...
Conference Paper
Full-text available
Automated recommender systems predict user preferences by applying machine learning techniques to data on products, users, and past user preferences for products. Such systems have become increasingly popular in entertainment and e-commerce domains, but have thus far had little success in information-seeking domains such as identifying published re...
Conference Paper
Full-text available
Recommender systems have been shown to help users nd items of interest from among a large pool of potentially in- teresting items. Inuenc e is a measure of the eect of a user on the recommendations from a recommender system. In- uence is a powerful tool for understanding the workings of a recommender system. Experiments show that users have widely...
Conference Paper
Full-text available
Online communities need regular maintenance activities such as moderation and data input, tasks that typically fall to community owners. Communities that allow all members to participate in maintenance tasks have the potential to be more robust and valuable. A key challenge in creating member-maintained communities is building interfaces, algorithm...
Article
Recommender systems are an increasingly popular tool used by many consumers to help deal with information overload in today's marketplace. At the cost of some personal information, these systems are able to personalize a user's online experience and guide them toward making better decisions. This paper examines two issues relating to pri-vacy in re...
Article
Recommender systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of th...
Article
Full-text available
Recommender systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of th...
Conference Paper
Full-text available
The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine collaborative filtering and content-based filtering to recommend research pap...
Conference Paper
Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscr...
Article
Full-text available
Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. There are several important challenges that interface designers must overcome on mobile devices: Providing sufficient value to attract prospective wireless users, handling occasionall...
Conference Paper
Full-text available
Recommender systems build user models to help users find the items they will find most interesting from among many available items. One way to build such a model is to ask the user to rate a selection of items. The choice of items selected affects the quality of the user model generated. In this paper, we explore the effects of letting the user par...
Article
We investigate th use of dimensionality reduction to improve th e performance for a new class of data analysis software called "recommender systems". Recommender systems apply knowledge discovery tech:N4fl4 to th problem of making personalized product recommendations during a live customer interaction. T h tremendous growth of customers and product...
Article
Recommender syPx4fl apply knowledge discovery techniques to the problem of making personalized product recommendations during a live customer interaction. These sye ems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success in E-commerce nowaday s. The tremendous growth of customers and products in r...
Article
Full-text available
Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendati...
Article
Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect user...
Article
Full-text available
Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that...
Conference Paper
Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendati...
Conference Paper
Full-text available
Recommender systems help users sort through vast quantities of information. Sometimes, however, users do not know if they can trust the recommendations they receive. Adding a confidence metric has the potential to improve user satisfaction and alter user behavior in a recommender system. We performed an experiment to measure the effects of a confid...
Article
As the web rapidly evolved into an immense repository of content, human users discovered that they could no longer effectively identify the content of most interest to them. Several approaches developed for improving our ability to find content. Syntactic search engines helped index and rapidly scan millions of pages for keywords, but we quickly le...