Publication statistics

Pub. period:2007-2012
Pub. count:8
Number of co-authors:25



Co-authors

Number of publications with 3 favourite co-authors:

John Riedl:4
Rowan Nairn:2
Yuqing Ren:2

 

 

Productive colleagues

Jilin Chen's 3 most productive colleagues in number of publications:

Loren Terveen:69
Michael J. Muller:65
John Riedl:61
 
 
 

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Jilin Chen

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I'm a PhD Candidate at GroupLens Research, University of Minnesota. I build systems that help people find useful information in social media by inferring interest, influence, and social relationships from "digital traces" that people left online. I also analyze the dynamics of online groups in social media using social psychology theories. In recent years I have also been lucky enough to work as interns for various industry research labs, including Xerox PARC, IBM Research, and Microsoft Research Asia.

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Publications by Jilin Chen (bibliography)

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2012
 
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Wang, Loxley Sijia, Chen, Jilin, Ren, Yuqing and Riedl, John (2012): Searching for the Goldilocks zone: trade-offs in managing online volunteer groups. In: Proceedings of ACM CSCW12 Conference on Computer-Supported Cooperative Work 2012. pp. 989-998.

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 WikiProjects to address these questions. Our results reveal two critical trade-offs in managing online volunteer groups. First, factors that increase member productivity, measured by the number of edits on Wikipedia articles, also increase likelihood of withdrawal from contributing, perhaps due to feelings of mission accomplished or burnout. Second, individual membership in multiple projects has mixed effects. It decreases the amount of work editors contribute to both the individual projects and Wikipedia as a whole. It increases withdrawal for each individual project yet reduces withdrawal from Wikipedia. We discuss how our findings expand existing theories to fit the online context and inform the design of new tools to improve online volunteer work.

© All rights reserved Wang et al. and/or ACM Press

2011
 
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Chen, Jilin, Nairn, Rowan and Chi, Ed (2011): Speak little and well: recommending conversations in online social streams. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 217-226.

Conversation is a key element in online social streams such as Twitter and Facebook. However, finding interesting conversations to read is often a challenge, due to information overload and differing user preferences. In this work we explored five algorithms that recommend conversations to Twitter users, utilizing thread length, topic and tie-strength as factors. We compared the algorithms through an online user study and gathered feedback from real Twitter users. In particular, we investigated how users' purposes of using Twitter affect user preferences for different types of conversations and the performance of different algorithms. Compared to a random baseline, all algorithms recommended more interesting conversations. Further, tie-strength based algorithms performed significantly better for people who use Twitter for social purposes than for people who use Twitter for informational purpose only.

© All rights reserved Chen et al. and/or their publisher

2010
 
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Chen, Jilin, Ren, Yuqing and Riedl, John (2010): The effects of diversity on group productivity and member withdrawal in online volunteer groups. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 821-830.

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 other hand, individual differences can lead to conflict and diminished performance. In this paper, we examine the effects of group diversity on the amount of work accomplished and on member withdrawal behaviors in the context of WikiProjects. We find that increased diversity in experience with Wikipedia increases group productivity and decreases member withdrawal -- up to a point. Beyond that point, group productivity remains high, but members are more likely to withdraw. Strikingly, no such diminishing returns were observed for differences in member interest, which increases productivity and decreases member withdrawal in a linear fashion. Our results suggest that the low visibility of individual differences in online groups may allow them to harvest more of the benefits of diversity while bearing less of the cost. We discuss how our findings can inform further research of online collaboration.

© All rights reserved Chen et al. and/or their publisher

 
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Chen, Jilin, Nairn, Rowan, Nelson, Les, Bernstein, Michael and Chi, Ed H. (2010): Short and tweet: experiments on recommending content from information streams. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 1185-1194.

More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the

© All rights reserved Chen et al. and/or their publisher

 
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Bernstein, Michael S., Suh, Bongwon, Hong, Lichan, Chen, Jilin, Kairam, Sanjay and Chi, Ed H. (2010): Eddi: interactive topic-based browsing of social status streams. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 303-312.

Twitter streams are on overload: active users receive hundreds of items per day, and existing interfaces force us to march through a chronologically-ordered morass to find tweets of interest. We present an approach to organizing a user's own feed into coherently clustered trending topics for more directed exploration. Our Twitter client, called Eddi, groups tweets in a user's feed into topics mentioned explicitly or implicitly, which users can then browse for items of interest. To implement this topic clustering, we have developed a novel algorithm for discovering topics in short status updates powered by linguistic syntactic transformation and callouts to a search engine. An algorithm evaluation reveals that search engine callouts outperform other approaches when they employ simple syntactic transformation and backoff strategies. Active Twitter users evaluated Eddi and found it to be a more efficient and enjoyable way to browse an overwhelming status update feed than the standard chronological interface.

© All rights reserved Bernstein et al. and/or their publisher

2009
 
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Chen, Jilin, Geyer, Werner, Dugan, Casey, Muller, Michael J. and Guy, Ido (2009): Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 201-210.

This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.

© All rights reserved Chen et al. and/or ACM Press

2007
 
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Priedhorsky, Reid, Chen, Jilin, Lam, Shyong (Tony) K., Panciera, Katherine, Terveen, Loren and Riedl, John (2007): Creating, destroying, and restoring value in wikipedia. In: GROUP07: International Conference on Supporting Group Work 2007. pp. 259-268.

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 frequent editors and that this majority is increasing. Similarly, using the same impact measure, we show that the probability of a typical article view being damaged is small but increasing, and we present empirically grounded classes of damage. Finally, we make policy recommendations for Wikipedia and other wikis in light of these findings.

© All rights reserved Priedhorsky et al. and/or ACM Press

 Cited in the following chapter:

Social Computing: [/encyclopedia/social_computing.html]


 
 
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Kapoor, Nishikant, Chen, Jilin, Butler, John T., Fouty, Gary C., Stemper, James A., Riedl, John and Konstan, Joseph A. (2007): TechLens: a researcher's desktop. In: Proceedings of the 2007 ACM Conference on Recommender Systems 2007. pp. 183-184.

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 through implicit feedback, builds correlations among them, and uses that information to generate recommendations that match the user's profile. It gives users control over which parts of their profile of known citations are used in forming recommendations for new articles. This demonstration is a prototype that showcases some of the tools and services that TechLens offers to the users of digital libraries.

© All rights reserved Kapoor et al. and/or ACM Press

 
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