Sunnyvale, California, United States
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Experience & Education

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Publications

  • Discovering Geographical Topics in the Twitter Stream

    The proceedings of the 21th International Conference on World Wide Web (WWW 2012)

    Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news, eyewitness accounts, individual expression, and protest groups. Recently, Twitter, along with other online social networking services such as Foursquare, Gowalla, Facebook and Yelp, have started supporting location services in their messages, either explicitly, by letting users choose their places, or implicitly, by enabling geo-tagging, which is to associate messages with…

    Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news, eyewitness accounts, individual expression, and protest groups. Recently, Twitter, along with other online social networking services such as Foursquare, Gowalla, Facebook and Yelp, have started supporting location services in their messages, either explicitly, by letting users choose their places, or implicitly, by enabling geo-tagging, which is to associate messages with latitudes and longitudes. This functionality allows researchers to address an exciting set of questions: 1) How is information created and shared across geographical locations, 2) How do spatial and linguistic characteristics of people vary across regions, and 3) How to model human mobility. Although many attempts have been made for tackling these problems, previous methods are either complicated to be implemented or oversimplified that cannot yield reasonable performance. It is a challenge task to discover topics and identify users' interests from these geo-tagged messages due to the sheer amount of data and diversity of language variations used on these location sharing services. In this paper we focus on Twitter and present an algorithm by modeling diversity in tweets based on topical diversity, geographical diversity, and an interest distribution of the user. Furthermore, we take the Markovian nature of a user's location into account. Our model exploits sparse factorial coding of the attributes, thus allowing us to deal with a large and diverse set of covariates efficiently. Our approach is vital for applications such as user profiling, content recommendation and topic tracking. We show high accuracy in location estimation based on our model. Moreover, the algorithm identifies interesting topics based on location and language.

    Other authors
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Patents

  • Data Mining Unlearnable Data Sets

    Issued US 20080027886

    This invention concerns data mining, that is the extraction of information, from "unlearnable" data sets. In particular it concerns apparatus and a method for this purpose. The invention involves creating a finite training sample from the data set (14). Then training (50) a learning device (32) using a supervised learning algorithm to predict labels for each item of the training sample. Then processing other data from the data set with the trained learning device to predict labels and…

    This invention concerns data mining, that is the extraction of information, from "unlearnable" data sets. In particular it concerns apparatus and a method for this purpose. The invention involves creating a finite training sample from the data set (14). Then training (50) a learning device (32) using a supervised learning algorithm to predict labels for each item of the training sample. Then processing other data from the data set with the trained learning device to predict labels and determining whether the predicted labels are better (learnable) or worse (anti-learnable) than random guessing (52). And, using a reverser (34) to apply negative weighting to the predicted labels if it is worse (anti-learnable) (54).

    Other inventors
    • Adam Kowalczyk
    • Cheng Soon Ong

  • Utilizing Offline Clusters for Realtime Clustering of Search Results

    Filed US 12/835,954

    Other inventors

  • Term Weighting for Contextual Advertising

    Filed US 20110093331

    A contextual advertising system selects online advertisements for display on a network location. The system may transform page content of a page received in a platform over a network into a textual representation. In addition, the system may transform received site content of a site into a site signature. The site includes the page. The system then may correct the textual representation utilizing the site signature to produce modified textual representation. The system may utilize the modified…

    A contextual advertising system selects online advertisements for display on a network location. The system may transform page content of a page received in a platform over a network into a textual representation. In addition, the system may transform received site content of a site into a site signature. The site includes the page. The system then may correct the textual representation utilizing the site signature to produce modified textual representation. The system may utilize the modified textual representation to select an online advertisement. Considering a page in the context of the entire website to which it belongs leads to better understanding and interpretation of the page topic(s) and thus yields more accurate ad matching.

    Other inventors

Languages

  • English

    Native or bilingual proficiency

  • German

    Native or bilingual proficiency

  • Italian

    Professional working proficiency

  • Spanish

    Limited working proficiency

  • French

    Limited working proficiency

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