Shilad Sen

Collaborative filtering recommender systems

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.

Tagsplanations: explaining recommendations using tags

While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the users sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.

WP:clubhouse?: an exploration of Wikipedia's gender imbalance

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 Wikipedias legion of volunteer editors are female. In the current work, we present a scientific exploration of the gender imbalance in the English Wikipedias population of editors. We look at the nature of the imbalance itself, its effects on the quality of the encyclopedia, and several conflict-related factors that may be contributing to the gender gap. Our findings confirm the presence of a large gender gap among editors and a corresponding gender-oriented disparity in the content of Wikipedias articles. Further, we find evidence hinting at a culture that may be resistant to female participation.

The quest for quality tags

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 akey dimension of an entity to those that are profane, useless, or unintelligible. Tagging systems must often select a subset of available tags to display to users due to limited screen space. Because users often spread tags they have seen, selecting good tags not only improves an individuals view of tags, it also encourages them to create better tags in the future. We explore implicit (behavioral) and explicit (rating) mechanisms for determining tag quality. Based on 102,056 tag ratings and survey responses collected from 1,039 users over 100 days, we offer simple suggestions to designers of online communities to improve the quality of tags seen by their users.

The Tag Genome: Encoding Community Knowledge to Support Novel Interaction

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 machine learning approach for computing the tag genome, and we evaluate several learning models on a ground truth dataset provided by users. We describe an application of the tag genome called Movie Tuner which enables users to navigate from one item to nearby items along dimensions represented by tags. We present the results of a 7-week field trial of 2,531 users of Movie Tuner and a survey evaluating users’ subjective experience. Finally, we outline the broader space of applications of the tag genome.

FeedMe: a collaborative alert filtering system

As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naïve Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.

Learning to recognize valuable tags

Many websites use tags as a mechanism for improving item metadata through collective user effort. 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 selection algorithms that choose the tags that sites display. Based on 225,000 ratings and survey responses, we conduct offline analyses of 21 tag selection algorithms. We select the three best performing algorithms from our offline analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we offer tagging system designers advice about tag selection algorithms.

Recommenders everywhere:: the WikiLens community-maintained recommender system

Suppose you have a passion for items of a certain type, 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 infrastructure, a large community of contributors, or even a full catalog of items? WikiLens is open source software that enables anyone, anywhere to start a community-maintained recommender around any type of item. We introduce five principles for community-maintained recommenders that address the two key issues: (1) community contribution of items and associated information; and (2) finding items of interest. Since all recommender communities start small, we look at feasibility and utility in the small world, one with few users, few items, few ratings. We describe the features of WikiLens, which are based on our principles, and give lessons learned from two years of experience running wikilens.org.

Gender Representation on Journal Editorial Boards in the Mathematical Sciences

We study gender representation on the editorial boards of 435 journals in the mathematical sciences. Women are known to comprise approximately 15% of tenure-stream faculty positions in doctoral-granting mathematical sciences departments in the United States. Compared to this group, we find that 8.9% of the 13067 editorships in our study are held by women. We describe group variations within the editorships by identifying specific journals, subfields, publishers, and countries that significantly exceed or fall short of this average. To enable our study, we develop a semi-automated method for inferring gender that has an estimated accuracy of 97.5%. Our findings provide the first measure of gender distribution on editorial boards in the mathematical sciences, offer insights that suggest future studies in the mathematical sciences, and introduce new methods that enable large-scale studies of gender distribution in other fields.

Digital Hegemonies: The Localness of Search Engine Results

Every day, billions of Internet users rely on search engines to find information about places to make decisions about tourism, shopping, and countless other economic activities. In an opaque process, search engines assemble digital content produced in a variety of locations around the world and make it available to large cohorts of consumers. Although these representations of place are increasingly important and consequential, little is known about their characteristics and possible biases. Analyzing a corpus of Google search results generated for 188 capital cities, this article investigates the geographic dimension of search results, focusing on searches such as “Lagos” and “Rome” on different localized versions of the engine. This study answers these questions: To what degree is this city-related information locally produced and diverse? Which countries are producing their own representations and which are represented by others? Through a new indicator of localness of search results, we identify the factors that contribute to shape this uneven digital geography, combining several development indicators. The development of the publishing industry and scientific production appears as a fairly strong predictor of localness of results. This empirical knowledge will support efforts to curb the digital divide, promoting a more inclusive, democratic information society.