Zerozero88 Twitter URL Item Recommender

•

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Ed Chi

Technology

+University of Palo Alto Research Center! ^MIT CSAIL!
Minnesota!

8/23/10 CHI2010 ZeroZero88.com 1

•  Social
Information
Streams!

8/23/10 CHI2010 ZeroZero88.com 2

Follower and Followee
!

follows

Follower Followees

URL Sources

Topic Relevance Scores User Topic Profiles

Social Network Scores Local Social Network

Recommendation Engine

 Multiply scores
 Rank URLs using multiplied scores
 Recommend highest ranked URLs

Component Possible Design Choices

URL Sources FoF (followee-of-followees)
Popular

8/23/10 CHI2010 ZeroZero88.com 9

Component Possible Design Choices

Topic Relevance Self-Topic
Scores Followee-Topic
None

8/23/10 CHI2010 ZeroZero88.com 12

Component Possible Design Choices

Social Network Social Voting
Scores None

8/23/10 CHI2010 ZeroZero88.com 14

Component Possible Design Choices

URL Sources FoF (followee-of-followees)
Popular
Topic Self-Topic
Relevance Followee-Topic Recommendation Engine
Scores None
 Multiply scores
Social Social Voting  Rank URLs using multiplied scores
Network None  Recommend highest ranked URLs
Scores

8/23/10 CHI2010 ZeroZero88.com 15

Component Possible Design Choices

URL Sources FoF (followee-of-followees)
Popular
Topic Relevance Self-Topic
Scores Followee-Topic
None
Social Network Social Voting
Scores None

(beta=0.22, Z=1.78, p=.08)

8/23/10 CHI2010 ZeroZero88.com 19

Algorithms Differ Not Only in Accuracy!
!

Acknowledgements!

•  ASC folks at PARC!
•  All users of Zerozero88.com!
•  My labmates at the University for helping me
prepare the presentation!

•  … and reach me at jilin@cs.umn.edu for
questions!!

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Model-Driven Research in Social Computing Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors. By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building.

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Ed H. Chi Google Research (Work done at Xerox PARC) CSCL2011 Keynote Abstract: Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors. Indeed, increasingly, new social online resources such as social bookmarking sites and Wikis are becoming central in eLearning. By studying them, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for social computing and social learning. -----

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http://forum.stanford.edu/events/2012mobi.php Title: Location and Language in Social Media Ed H. Chi Staff Research Scientist, Google Research (work done at [Xerox] PARC) Abstract: Despite the widespread adoption of social media internationally, little research has investigated the differences among users of different languages. Moreover, we know relatively little about how people reveal their location information. In this talk, I will outline our recent characterization studies on how users of differing geographical locations and languages use social media. First, on geographical location: We found that 34% of users did not provide real location information in Twitter, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. 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. Second, on language, Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools. Homepage: edchi.net Speaker Bio: Ed H. Chi is a Staff Research Scientist at Google. Until recently, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. With 20 patents and over 90 research articles, his most well-known past project is the study of Information Scent --- understanding how users navigate and understand the Web and information environments. He also led a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines, and has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.

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Keynote talk given at the International Conference on Asia-Pacific Digital Libraries (ICADL) 2008. December, 2008 in Bali, Indonesia ICADL 2008 link here We are experiencing the new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by Wikipedia and del.icio.us, Web 2.0 environments are turning people into social information foragers and sharers. Users interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam." PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, sociology, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Web 2.0 systems like social tagging, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale. In this talk, we summarize recent PARC work and early findings on: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.

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HCI have long moved beyond the evaluation setting of a single user sitting in front of a single desktop computer, yet many of our fundamentally held viewpoints about evaluation continues to be ruled by outdated biases derived from this legacy. We need to engage with real users in 'Living Laboratories', in which researchers either adopt or create functioning systems that are used in real settings. These new experimental platforms will greatly enable researchers to conduct evaluations that span many users, places, time, location, and social factors in ways that are unimaginable before.

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Learning item and user representations with sparse data in recommender systems Ed H. Chi Google Inc. Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users. In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.

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Model-Driven Research in Social Computing Abstract: Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors. By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building Bio: Ed H. Chi is a Staff Research Scientist at Google, working on the Google+ project. Very recently, Ed was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. With 20 patents and over 80 research articles, his most well-known past project is the study of Information Scent — understanding how users navigate and understand the Web and information environments. Most recently, he leads a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines. He has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.

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Zerozero88 Twitter URL Item Recommender

1. +University of Palo Alto Research Center! ^MIT CSAIL! Minnesota! 8/23/10 CHI2010 ZeroZero88.com 1

2. •  Social Information Streams! 8/23/10 CHI2010 ZeroZero88.com 2

3. 8/23/10 CHI2010 ZeroZero88.com 3

4. Follower and Followee ! follows Follower Followees

5. 8/23/10 CHI2010 ZeroZero88.com 5

6. 8/23/10 CHI2010 ZeroZero88.com 6

7. 8/23/10 CHI2010 ZeroZero88.com 7

8. URL Sources Topic Relevance Scores User Topic Profiles Social Network Scores Local Social Network Recommendation Engine  Multiply scores  Rank URLs using multiplied scores  Recommend highest ranked URLs

9. Component Possible Design Choices URL Sources FoF (followee-of-followees) Popular 8/23/10 CHI2010 ZeroZero88.com 9

10. URL Sources Topic Relevance Scores User Topic Profiles Social Network Scores Local Social Network Recommendation Engine  Multiply scores  Rank URLs using multiplied scores  Recommend highest ranked URLs

11. 8/23/10 CHI2010 ZeroZero88.com 11

12. Component Possible Design Choices Topic Relevance Self-Topic Scores Followee-Topic None 8/23/10 CHI2010 ZeroZero88.com 12

13. URL Sources Topic Relevance Scores User Topic Profiles Social Network Scores Local Social Network Recommendation Engine  Multiply scores  Rank URLs using multiplied scores  Recommend highest ranked URLs

14. Component Possible Design Choices Social Network Social Voting Scores None 8/23/10 CHI2010 ZeroZero88.com 14

15. Component Possible Design Choices URL Sources FoF (followee-of-followees) Popular Topic Self-Topic Relevance Followee-Topic Recommendation Engine Scores None  Multiply scores Social Social Voting  Rank URLs using multiplied scores Network None  Recommend highest ranked URLs Scores 8/23/10 CHI2010 ZeroZero88.com 15

16. 8/23/10 CHI2010 ZeroZero88.com 16

17. 8/23/10 CHI2010 ZeroZero88.com 17

18. 8/23/10 CHI2010 ZeroZero88.com 18

19. Component Possible Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Self-Topic Scores Followee-Topic None Social Network Social Voting Scores None (beta=0.22, Z=1.78, p=.08) 8/23/10 CHI2010 ZeroZero88.com 19

20. Component Possible Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Self-Topic Scores Followee-Topic None Social Network Social Voting Scores None (beta=0.58, Z=4.95, p<.001) (beta=0.27, Z=2.48, p=.01) 8/23/10 CHI2010 ZeroZero88.com 20

21. Component Possible Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Self-Topic Scores Followee-Topic None Social Network Social Voting Scores None (beta=1.02, Z=6.53, p<.001) 8/23/10 CHI2010 ZeroZero88.com 21

22. Component Possible Design Choices URL Sources FoF (followee-of-followees) Popular Topic Relevance Self-Topic Scores Followee-Topic None Social Network Social Voting Scores None 8/23/10 CHI2010 ZeroZero88.com 22

23. 8/23/10 CHI2010 ZeroZero88.com 23

24. 8/23/10 CHI2010 ZeroZero88.com 24

25. 8/23/10 CHI2010 ZeroZero88.com 25

26. Algorithms Differ Not Only in Accuracy! !

27. Acknowledgements! •  ASC folks at PARC! •  All users of Zerozero88.com! •  My labmates at the University for helping me prepare the presentation! •  … and reach me at jilin@cs.umn.edu for questions!!

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