Ed H. Chi

Online search and item recommendation systems are often based on being able to correctly label items with topical keywords. Typically, topical labelers analyze the main text associated with the item, but social media posts are often multimedia in nature and contain contents beyond the main text. Topic labeling for social media posts is therefore an important open problem for supporting effective social media search and recommendation. In this work, we present a novel solution to this problem… Show more

Online search and item recommendation systems are often based on being able to correctly label items with topical keywords. Typically, topical labelers analyze the main text associated with the item, but social media posts are often multimedia in nature and contain contents beyond the main text. Topic labeling for social media posts is therefore an important open problem for supporting effective social media search and recommendation. In this work, we present a novel solution to this problem for Google+ posts, in which we integrated a number of different entity extractors and annotators, each responsible for a part of the post (e.g. text body, embedded picture, video, or web link). To account for the varying quality of different annotator outputs, we first utilized crowdsourcing to measure the accuracy of individual entity annotators, and then used supervised machine learning to combine different entity annotators based on their relative accuracy. Evaluating using a ground truth data set, we found that our approach substantially outperforms topic labels obtained from the main text, as well as naive combinations of the individual annotators. By accurately applying topic labels according to their relevance to social media posts, the results enables better search and item recommendation. Show less

Tabbed web browsing interfaces enable users to multi-task and easily switch between open web pages. However, tabbed browsing is difficult for mobile web browsers due to the limited screen space and the reduced precision of touch. We present an experiment comparing Safari's pages-based switching interface using horizontal swiping gestures with the stacked cards-based switching interface using vertical scrolling gestures, introduced by Chrome. The results of our experiment show that cards-based… Show more

Tabbed web browsing interfaces enable users to multi-task and easily switch between open web pages. However, tabbed browsing is difficult for mobile web browsers due to the limited screen space and the reduced precision of touch. We present an experiment comparing Safari's pages-based switching interface using horizontal swiping gestures with the stacked cards-based switching interface using vertical scrolling gestures, introduced by Chrome. The results of our experiment show that cards-based switching interface allows for faster switching and is less frustrating, with no significant effect on error rates. We generalize these findings, and provide design implications for mobile information spaces. Show less

Consumer review sites and recommender systems typically rely on a large volume of user-contributed ratings, which makes rating acquisition an essential component in the design of such systems. User ratings are then summarized to provide an aggregate score representing a popular evaluation of an item. An inherent problem in such summarization is potential bias due to raters’ self-selection and heterogeneity in terms of experiences, tastes and rating scale interpretations. There are two major… Show more

Consumer review sites and recommender systems typically rely on a large volume of user-contributed ratings, which makes rating acquisition an essential component in the design of such systems. User ratings are then summarized to provide an aggregate score representing a popular evaluation of an item. An inherent problem in such summarization is potential bias due to raters’ self-selection and heterogeneity in terms of experiences, tastes and rating scale interpretations. There are two major approaches to collecting ratings, which have different advantages and disadvantages. One is to allow a large number of volunteers to choose and rate items directly (a method employed by e.g. Yelp and Google Places). Alternatively, a panel of raters may be maintained and invited to rate a predefined set of items at regular intervals (such as in Zagat Survey). The latter approach arguably results in more consistent reviews and reduced selection bias, however, at the expense of much smaller coverage (fewer rated items). In this paper, we examine the two different approaches to collecting user ratings of restaurants and explore the question of whether it is possible to reconcile them. Specifically, we study the problem of inferring the more calibrated Zagat Survey ratings (which we dub “expert ratings”) from the user-contributed ratings (“grassroots”) in Google Places. To achieve this, we employ latent factor models and provide a probabilistic treatment of the ordinal ratings. We can predict Zagat Survey ratings accurately from ad hoc user-generated ratings by employing joint optimization. Furthermore, the resulting model show that users become more discerning as they submit more ratings. We also describe an approach towards cross-city recommendations, answering questions such as “What is the equivalent of the Per Se restaurant in Chicago?” Show less

Online social networks have become indispensable tools for information sharing, but existing "all-or-nothing" models for sharing have made it difficult for users to target information to specific parts of their networks. In this paper, we study Google+, which enables users to selectively share content with specific "Circles" of people. Through a combination of log analysis with surveys and interviews, we investigate how active users organize and select audiences for shared content. We find that… Show more

Online social networks have become indispensable tools for information sharing, but existing "all-or-nothing" models for sharing have made it difficult for users to target information to specific parts of their networks. In this paper, we study Google+, which enables users to selectively share content with specific "Circles" of people. Through a combination of log analysis with surveys and interviews, we investigate how active users organize and select audiences for shared content. We find that these users frequently engaged in selective sharing, creating circles to manage content across particular life facets, ties of varying strength, and interest-based groups. Motivations to share spanned personal and informational reasons, and users frequently weighed "limiting" factors (e.g. privacy, relevance, and social norms) against the desire to reach a large audience. Our work identifies implications for the design of selective sharing mechanisms in social networks. Show less

Little research exists on one of the most common, oldest, and most utilized forms of online social geographic information: the 'location' field found in most virtual community user profiles. We performed the first in-depth study of user behavior with regard to the location field in Twitter user profiles. We found that 34% of users did not provide real location information, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. When… Show more

Little research exists on one of the most common, oldest, and most utilized forms of online social geographic information: the 'location' field found in most virtual community user profiles. We performed the first in-depth study of user behavior with regard to the location field in Twitter user profiles. We found that 34% of users did not provide real location information, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. When users did input their location, they almost never specified it at a scale any more detailed than their city. In order to determine whether or not natural user behaviors have a real effect on the 'locatability' of users, we performed a simple machine learning experiment to determine whether we can identify a user's location by only looking at what that user tweets. We found that a user's country and state can in fact be determined easily with decent accuracy, indicating that users implicitly reveal location information, with or without realizing it. Implications for location-based services and privacy are discussed. Show less

Corporate wikis are affected by poor adoption rates. The
high interaction costs required to organize and maintain
information in these wikis are a key factor that limits
broader adoption. We present VisualWikiCurator, a wiki
extension designed to lower such costs by (a)
recommending new content to easily update a wiki page,
and (b) extracting structured data from the wiki page while
providing new alternative visualizations of the data. The
visualizations of… Show more

Corporate wikis are affected by poor adoption rates. The
high interaction costs required to organize and maintain
information in these wikis are a key factor that limits
broader adoption. We present VisualWikiCurator, a wiki
extension designed to lower such costs by (a)
recommending new content to easily update a wiki page,
and (b) extracting structured data from the wiki page while
providing new alternative visualizations of the data. The
visualizations of extracted semantic data act both as
alternative views and as tools to organize the page content.
Since no information extraction algorithm is perfect with
generic unstructured data, we use a mixed-initiative
approach to allow users to refine machine-extracted
metadata and easily re-organize the content in wiki pages. Show less

This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the… Show more

This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype. Show less

Web Usage Mining enables new understanding of user goals on the Web. This understanding has broad applications, and traditional mining techniques such as association rules have been used in business applications. We have developed an automated method to directly infer the major groupings of user traffic on a Web site [Heer01]. We do this by utilizing multiple data features in a clustering analysis. We have performed an extensive, systematic evaluation of the proposed approach, and have… Show more

Web Usage Mining enables new understanding of user goals on the Web. This understanding has broad applications, and traditional mining techniques such as association rules have been used in business applications. We have developed an automated method to directly infer the major groupings of user traffic on a Web site [Heer01]. We do this by utilizing multiple data features in a clustering analysis. We have performed an extensive, systematic evaluation of the proposed approach, and have discovered that certain clustering schemes can achieve categorization accuracies as high as 99% [Heer02b]. In this paper, we describe the further development of this work into a prototype service called LumberJack, a push-button analysis system that is both more automated and accurate than past systems. Show less

Several visualizations have emerged which attempt to
visualize all or part of the World Wide Web. Those
visualizations, however, fail to present the dynamically
changing ecology of users and documents on the Web. We
present new techniques for Web Ecology and Evolution
Visualization (WEEV). Disk Trees represent a discrete
time slice of the Web ecology. A collection of Disk Trees
forms a Time Tube, representing the evolution of the Web
over longer periods of time. These… Show more

Several visualizations have emerged which attempt to
visualize all or part of the World Wide Web. Those
visualizations, however, fail to present the dynamically
changing ecology of users and documents on the Web. We
present new techniques for Web Ecology and Evolution
Visualization (WEEV). Disk Trees represent a discrete
time slice of the Web ecology. A collection of Disk Trees
forms a Time Tube, representing the evolution of the Web
over longer periods of time. These visualizations are
intended to aid authors and webmasters with the
production and organization of content, assist Web surfers
making sense of information, and help researchers
understand the Web. Show less