James Caverlee

Professor
Department of Computer Science and Engineering
Texas A&M University
infolab
[my_last_name] 'at' cse.tamu.edu

Industry engagement: Amazon Scholar

Research: My research targets topics from recommender systems, social media, information retrieval, data mining, and emerging networked information systems. My work typically focuses on:

threats to these systems, including social spam/bots, crowd-powered strategic manipulation, collective attention threats, attacks on search engines, and other emerging threats; and
new beneficial algorithms and frameworks for these systems, including our work on fairness in recommendation, geo-social characteristics of social media, user modeling, expert detection, and neural models of ranking and recommendation.

About me: I received my Ph.D. in Computer Science from Georgia Tech in 2007, co-advised by Ling Liu (CS) and Bill Rouse (ISYE). Before that, I earned two M.S. degrees from Stanford University: one in Computer Science in 2001 and one in Engineering-Economic Systems & Operations Research in 2000. My undergraduate degree is a B.A. in Economics (magna cum laude) from Duke University in 1996. I joined the faculty at Texas A&M in 2007. I spent most of my sabbatical in 2015 at Google as a Visiting Scientist in Ed Chi's group. I've been honored to receive an NSF CAREER award, DARPA Young Faculty award, a AFOSR Young Investigator award, as well as several teaching awards. Recently co-organized WSDM 2020 with Xia "Ben" Hu. Thank you to everyone who helped make this a successful conference!

Acknowledgments: Our lab has been generously supported by NSF, DARPA, AFOSR, Google, Amazon, the Texas A&M College of Engineering, and Texas A&M. Thank you!

Some recent projects:

Alexa Prize Taskbot Challenge.
Amazon Research Awards: Fairness in Recommendation without Demographics
NSF FAI: Towards Fairness in Deep Neural Networks with Learning Interpretation, with Xia "Ben" Hu, Na Zou, and Chaitanya Lakkimsetti.
NSF III: Small: Collaborative Research: Modeling and Managing Extremist Group Influence in Massive Social Media Networks, with Ruihong Huang (at Texas A&M) and Anna Squicciarini (at Penn State).
NSF EAGER: Fairness-Aware Personalized Recommendations
DARPA. Synthetic Prediction Markets with Algorithm Traders for Determining Experimental Reproducibility, with Frank Shipman and Xia “Ben” Hu.
NSF SaTC: CORE: Small: Adversarial Learning via Modeling Interpretation, with Xia “Ben” Hu and Guofei Gu.

umm ... — Or you may prefer: quarantine haircut, chilling out (pic by Hongning Wang), Wampa attack, thinking, coffee, guitar, Korean, bike, canoe, rainy, Gandalf, GRRM, dunno, younger me, fancy me, or collaborating (pic by Hongning Wang).

Publications

See also my publications at DBLP, Google Scholar, and at Semantic Scholar.

Enhancing User Personalization in Conversational Recommenders. The Web Conference (WWW) 2023.
Reinforced Sequence Training based Subjective Bias Correction. EACL 2023.
Closed-book Question Generation via Contrastive Learning. EACL 2023.
Evolution of Filter Bubbles and Polarization in News Recommendation (short paper). ECIR 2023.
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems. CIKM 2022.
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks. WWW 2022.
RES: A Replicability Estimation System (demo). AAAI 2022.
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning. AAAI 2022.
Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning. WSDM 2022.
Popularity Bias in Dynamic Recommendation. KDD 2021.
Identifying Hijacked Reviews. 4th Workshop on e-Commerce and NLP 2021.
Vibe Check: Social Resonance Learning for Enhanced Recommendation. ASONAM 2021.
Fairness among New Items in Cold Start Recommender Systems. SIGIR 2021.
Sequential Recommendation for Cold-start Users with Meta Transitional Learning (short paper). SIGIR 2021.
Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests. WWW 2021.
Towards Ongoing Detection of Linguistic Bias on Wikipedia (extended abstract). WikiWorkshop 2021.
Item Relationship Graph Neural Networks for E-Commerce IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021.
Session-based Recommendation with Hypergraph Attention Networks. SDM 2021.
Popularity-Opportunity Bias in Collaborative Filtering. WSDM 2021.
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. EMNLP 2020.
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge. EMNLP 2020.
Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. RecSys 2020.
Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning (short paper). RecSys 2020.
ADORE: Aspect Dependent Online REview Labeling for Review Generation. SIGIR 2020.
Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. SIGIR 2020.
Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. SIGIR 2020.
Next-item Recommendation with Sequential Hypergraphs. SIGIR 2020.
Adaptive Hierarchical Translation-based Sequential Recommendation (short paper). WWW 2020.
Addressing the Target Customer Distortion Problem in Recommender Systems (short paper). WWW 2020.
Recommending Music Curators: A Neural Style-Aware Approach. ECIR 2020.
Consistency-Aware Recommendation for User-Generated Item List Continuation. WSDM 2020.
User Recommendation in Content Curation Platforms. WSDM 2020.
Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion. WSDM 2020.
Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce. WSDM 2020.
Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations. WSDM 2020.
Tensor Completion Algorithms in Big Data Analytics. ACM TKDD 13(1): 6:1-6:48 (2019)
Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures. CIKM 2019.
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists. CIKM 2019.
Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence. CIKM 2019.
TOmCAT: Target-Oriented Crowd Review ATtacks and Countermeasures. ICWSM 2019.
A Large-Scale Study of ISIS Social Media Strategy. ICWSM 2019.
Improving Top-K Recommendation via Joint Collaborative Autoencoders (short paper). WWW 2019.
An Interpretable Neural Model with Interactive Stepwise Influence. PAKDD 2019.
Recurrent Recommendation with Local Coherence. WSDM 2019.
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation. ICDM 2018.
Fairness-Aware Tensor-Based Recommendation. CIKM 2018.
Quality-Aware Neural Complementary Item Recommendation. RecSys 2018.
Fairness-Aware Recommendation of Information Curators. 2nd FATREC Workshop on Responsible Recommendation (RecSys), 2018.
TrailMix: An Ensemble Recommender System for Playlist Curation and Continuation. RecSys Challenge 2018.
Vitriol on Social Media: Curation and Investigation. SocInfo 2018.
Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User Factorization. SIGIR 2018.
DisTenC: A Distributed Algorithm for Scalable Tensor Completion on Spark. ICDE 2018.
Neural Personalized Ranking for Image Recommendation. WSDM 2018.
Combating Crowdsourced Review Manipulators: A Neighborhood-Based Approach. WSDM 2018.
Measuring the Impact of ISIS Social Media Strategy (short paper). MIS2: Misinformation and Misbehavior Mining on the Web, workshop at WSDM 2018.
Location-Sensitive User Profiling Using Crowdsourced Labels. AAAI 2018.
Badly Evolved? Exploring Long-Surviving Suspicious Users on Twitter. SocInfo 2017.
Online Deception Detection Refueled by Real World Data Collection. RANLP 2017.
Multi-Aspect Streaming Tensor Completion. KDD 2017.
What Are You Known For? Learning User Topical Profiles with Implicit and Explicit Footprints. SIGIR 2017.
Crowdsourced App Review Manipulation (short paper). SIGIR 2017.
Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond (short paper). ICWSM 2017.
Dynamic Contract Design for Heterogenous Workers in Crowdsourcing for Quality Control (Applications and Experiences Track). ICDCS 2017.
On Local Expert Discovery via Geo-Located Crowds, Queries, and Candidates. ACM Transactions on Spatial Algorithms and Systems. November 2016.
Discovering What You're Known For: A Contextual Poisson Factorization Approach. RecSys 2016.
TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation. RecSys 2016. (best paper nominee)
Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information. CIKM 2016.
CrowdSelect: Increasing Accuracy of Crowdsourcing Tasks through Behavior Prediction and User Selection. CIKM 2016.
Community-Based Geospatial Tag Estimation. ASONAM 2016.
LExL: A Learning Approach for Local Expert Discovery on Twitter (short paper). ECIR 2016.
College Towns, Vacation Spots, and Tech Hubs: Using Geo-Social Media to Model and Compare Locations. AAAI 2016.
Exploiting Geo-Spatial Preference for Personalized Expert Recommendation. RecSys 2015.
Organic or Organized? Exploring URL Sharing Behavior. CIKM 2015.
BiasWatch: A Lightweight System for Discovering and Tracking Topic-Sensitive Opinion Bias in Social Media. CIKM 2015.
Uncovering Crowdsourced Manipulation of Online Reviews. SIGIR 2015.
Crowds, Gigs, and Super Sellers: A Measurement Study of a Supply-Driven Crowdsourcing Marketplace. ICWSM 2015.
A Noise-Filtering Approach for Spatio-Temporal Event Detection in Social Media. ECIR 2015.
Detecting Spam URLs in Social Media via Behavioral Analysis. ECIR 2015.
Who is the Barbecue King of Texas?: A Geo-Spatial Approach to Finding Local Experts. SIGIR 2014.
Spatio-Temporal Meme Prediction: Learning What Hashtags Will Be Popular Where. CIKM 2013.
Location Prediction in Social Media Based on Tie Strength. CIKM 2013.
Crowdturfers, Campaigns, and Social Media: Tracking and Revealing Crowdsourced Manipulation of Social Media. ICWSM 2013.
Campaign Extraction from Social Media. ACM TIST 2013.
A Content-Driven Framework for Geo-locating Microblog Users. ACM TIST 2013.
Combating Threats to Collective Attention in Social Media: An Evaluation. ICWSM 2013.
Spatio-Temporal Dynamics of Online Memes: A Study of Geo-Tagged Tweets. WWW 2013.
How Big is the Crowd? Event and Location Based Population Modeling in Social Media. Hypertext 2013 (best paper nominee).
Content-Based Crowd Retrieval on the Real-Time Web. CIKM 2012.
Spatial Influence vs. Community Influence: Modeling the Global Spread of Social Media. CIKM 2012.
Predicting Semantic Annotations on the Real-Time Web. HT 2012.
Detecting Collective Attention Spam. WebQuality Workshop (WWW), 2012.
Temporal Dynamics of Communities in Social Bookmarking Systems. Springer SNAM 2012.
Predicting Community Preference of Comments on the Social Web. Web Intelligence and Agent Systems: An International Journal 2012.
Toward Traffic-Driven Location-Based Web Search. CIKM 2011.
Content-Driven Detection of Campaigns in Social Media (short paper). CIKM 2011.
Exploring Millions of Footprints in Location Sharing Services. ICWSM 2011.
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter. ICWSM 2011.
Hierarchical Comments-Based Clustering. ACM SAC 2011.
Transient Crowd Discovery on the Real-Time Social Web. WSDM 2011.
You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users. CIKM 2010.
Uncovering Social Spammers: Social Honeypots + Machine Learning. SIGIR 2010.
Community-Based Ranking of the Social Web. HT 2010.
The SocialTrust Framework for Trusted Social Information Management: Architecture and Algorithms. Journal of Information Sciences 2010.
Ranking Comments on the Social Web. SocialCom 2009.
Probabilistic Generative Models of the Social Annotation Process. SocialCom 2009.
A Categorical Model for Discovering Latent Structure in Social Annotations. ICWSM 2009.
PageRank for ranking authors in co-citation networks. JASIST 2009.
A Parameterized Approach to Spam-Resilient Link Analysis of the Web. IEEE TPDS 2009.
Exploring Feedback Models in Interactive Tagging. Web Intelligence 2008.
Predicting Web Spam With HTTP Session Information. CIKM 2008.
Social Honeypots: Making Friends with a Spammer Near You. CEAS 2008.
SocialTrust: Tamper-Resilient Trust Establishment in Online Communities. JCDL 2008.
A Large-Scale Study of MySpace: Observations and Implications for Online Social Networks. ICWSM 2008.

2007 and earlier

Countering Web Spam with Credibility-Based Link Analysis. PODC 2007.
Characterizing Web Spam Using Content and HTTP Session Analysis. CEAS 2007.
Spam-Resilient Web Rankings via Influence Throttling. IPDPS 2007.
Development of Distance Measures for Process Mining, Discovery, and Integration. International Journal of Web Services Research (JWSR) 2007.
Workflow Management for Enterprise Transformation. Information, Knowledge, Systems Management (IKSM) 2007.
Service Class Driven Dynamic Source Discovery with DynaBot. International Journal of Web Services Research (JWSR) 2007.
Process Mining, Discovery and Integration using Distance Measures. IEEE ICWS 2006.
Distributed Query-Sampling: A Quality Conscious Approach. SIGIR 2006.
Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically. CEAS 2006.
Discovering Interesting Relationships among Deep Web Databases: A Source-Biased Approach. World Wide Web Journal: Internet and Web Information Systems (WWWJ) 2006.
XWRAPComposer: A Multi-Page Data Extraction Service. International Journal of Web Services Research (JWSR) 2006.
Domain-Specific Web Service Discovery with Service Class Descriptions. IEEE ICWS 2005.
XWRAPComposer: A Multi-Page Data Extraction Service for Bio-Computing Applications. IEEE SCC 2005.
QA-Pagelet: Data Preparation Techniques for Large Scale Data Analysis of the Deep Web. IEEE TKDE 2005.
A Methodical Approach to Extracting Meaningful Objects from Dynamic Web Pages. International Journal of Web and Grid Services (IJWGS) 2005.
Discovering and Ranking Web Services with BASIL: A Personalized Approach with Biased Focus. ICSOC 2004.
Probe, Cluster, and Discover: Focused Extraction of QA-Pagelets from the Deep Web. ICDE 2004.

Students

Current and Former PhD Students

Allen Lin
Haoran Liu
Karthic Madanagopal
Majid Alfifi
Maria Teleki
Xiangjue Dong
Zhuoer Wang
Ziwei Zhu (PhD, August 2022), Current employment: Tenure-track Assistant Professor at GMU
Yun He (PhD, August 2022), Current employment: Meta
Jianling Wang (PhD, May 2022), Current employment: Google Brain
Yin Zhang (PhD, May 2022), Current employment: Google Brain
Xing Zhao (PhD, August 2021), Current employment: Applied Scientist at Amazon
Parisa Kaghazgaran (PhD, December 2020), Current employment: Research Scientist at Amazon
Cheng Cao (PhD, December 2017), Current employment: Applied Scientist Manager at Amazon
Hancheng Ge (PhD, December 2017), Current employment: Senior Applied Scientist at Amazon
Haokai Lu (PhD, December 2017), Current employment: Senior Software Engineer at Google Brain
Wei Niu (PhD, December 2017), Current employment: Senior Applied Scientist at Amazon
Derek Zhiyuan Cheng (PhD, May 2014), Current employment: Senior Staff Software Engineer at Google Brain
Kyumin Lee (PhD, August 2013), Current employment: Associate Professor at Worcester Polytechnic Institute
Krishna Kamath (PhD, August 2013), Current employment: Senior Engineering Manager at Twitter
Elham Khabiri (PhD, May 2013), Current employment: Research Staff Member at IBM Research
Said Kashoob (PhD, May 2011), Current employment: Dean at Ibri College of Technology (Oman)
Brian Eoff (on leave), Current employment: Senior Software Engineer at Google AI
Jeremy Kelley (on leave), building the future

Current and Former MS Students

Rohan Chaudhury
Jinhao Pan
Laren Spear
Piyush Nayak
Han Zhang
Monika Daryani (MS, May 2021), First employment: Amazon
Haiping Xue (MS, May 2019), First employment: Facebook
Aaron Knodell (MS, May 2019), First employment:
Siddharth Verma (MS, December 2018), First employment: Amazon
Aniket Bonde (MS, December 2018), First employment: Uber
Tae Jun Jeon (MS, August 2018), First employment: Kakao
Rahul Bhagat (MS, May 2018), First employment: Amazon
Zhijiao Liu (MS, December 2015), First employment: Amazon
Ashraf Ibrahim (MS, December 2015), First employment:
Himanshu Barthwal (MS, August 2015), First employment: FactSet
Vardharaj Lakshminarasimhan (MS, December 2014), First employment: LinkedIn
Sindhuja Venkatesh (MS, December 2014), First employment: Amazon A9
Vandana Bachini (MS, December 2013), First employment: Google
Amir Fayazi (MS, December 2013), First employment: Google
Yuan Liang (MS, August 2013), First employment: Facebook
Prithivi Tamilarasan (MS, August 2013), First employment: eBay
Jeff McGee (MS, May 2013), First employment: ZipfWorks
Liangliang Wang (MCS, May 2011), First employment: Cisco
Srihari Ananthapadmanabhan (MCS, August 2010), First employment: Yahoo!
Vignesh Venkataraman (MCS, August 2010), First employment: Schlumberger
Karthik Ramachandran (MCS, August 2010), First employment: Amazon
Lichun Li (MCS, May 2010), First employment: Microsoft
Chiao-Fang Hsu (MS, December 2009), First employment: Amazon
Anupam Aggarwal (MS, December 2009), First employment: Yahoo!
Chandan Aggarwal (MCS, December 2009), First employment: Microsoft
Robert Graham (MCS, December 2008), First employment: University of Texas at Austin, Applied Research Labs

Current and Former Undergrads

Ronald Lee
Shri Mathavan
Jack Sebastian
Charles Im, 2022
Kareem Hirani, 2022
Joycelin Gu, 2022
Diva Kohli, 2022
Timo Feldman, 2022
Rakhi Shah, 2021
Akash Rao, 2021
Sagar Patel, 2020
Jonathan Innis, 2020
Benjamin Levinson, 2019
Victoria Wei, 2019
Kevin Nguyen, 2019
Nathan Villanueva, 2018
Nazif Ali, 2017
Jason Bolden, 2013

April Senner, Summer 2013
Kalil Armstrong, Summer 2012
Jason Bolden, Summer 2012
Bethany Waldmann, Summer 2009

Ryan Mendoza, 2022
Aditya Pethe, 2021-2022
Maria Teleki, 2020-2022
Habeeb Hooshmand, 2017-2018
Tyler Durden, 2017
Jared Russell, 2017
Matt Shubert 2012
Ananta Uprety, 2012
Joe Cabrera, 2011
Michael Jablonski, 2011
Matt Hillary, 2009
J. Chris Elgin, 2008

Teaching

Courses at Texas A&M

CSCE 489, Recommender Systems: Spring 2021
CSCE 320, Principles of Data Science: Fall 2020
CSCE 676, Data Mining and Analysis: Fall 2019, Fall 2021, Spring 2022
CSCE 438, Distributed Data-Intensive Systems: Spring 2014
CSCE 489, Data Science and Analytics: Fall 2016, Spring 2013
CSCE 670, Information Storage and Retrieval (grad): many times
CSCE 689, Internet-Scale Data Management: Fall 2010, Fall 2007
CSCE 470, Information Storage and Retrieval (undergrad): many times [a poem?]
UPAS 181, Computer Science in the Age of Twitter (1st-year seminar): Fall 2009

Other Teaching Experience

(Guest Lecturer) Georgia Tech, Introduction to Database Systems: Spring 2006, Instructor: Ling Liu
(Invited Lecturer) Emory University: Topics in Computer Science, Data Mining: Fall 2005, Instructor: Li Xiong
(Teaching Assistant) Georgia Tech, Database System Implementation: Spring 2005, Spring 2004, Instructor: Ling Liu
(Guest Lecturer) Georgia Tech, Data and Information Management: Spring 2004, Instructor: Brian Cooper
(Guest Lecturer) Georgia Tech, Advanced Internet Application Development: Spring 2004, Instructor: Ling Liu
(Teaching Assistant) Stanford University: Introduction to Economics: Fall 1999 to Spring 2001, Instructors: John Taylor and Marcelo Clerici-Arias

Service (not updated)

General co-chair: WSDM 2020
Program committee co-chair: ASONAM 2016, CollaborateCom 2011.
Senior program committee: KDD 2019, SIGIR 2019, SDM 2019, WSDM 2018, KDD 2018, CIKM 2017, KDD 2017, IJCAI 2016, CIKM 2016, CIKM 2015, ICWSM 2014.
Program committee: SIGIR 2017, ASONAM 2017, ICDCS 2017, ICWSM 2017, RecSys 2017, WSDM 2017, WWW 2017, HCOMP 2016, CIC 2016 ICDM 2016, ICWSM 2016, KDD 2016, SIGIR 2016, SocInfo 2016, WSDM 2016, WWW 2016, ICWSM 2015, IEEE CIC 2015 KDD 2015, PopInfo 2015 (co-located with KDD) SIGIR 2015, SocInfo 2015, SWDM 2015 (co-located with WWW) WSDM 2015, WWW 2015.
Tutorials co-chair: ICDCS 2017.
Workshop organizer: TAIA 2015 (co-located with SIGIR), WebQuality 2014 (co-located with WWW), DUBMOD 2013 (co-located with CIKM), DUBMOD 2012 (co-located with CIKM).
Referee: ACM TOIS, ACM TiiS, ACM TISSEC, ACM TSAS, ACM TWEB, IEEE TKDE, CACM.
Editorial board: IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Intelligent Systems, Social Network Analysis and Mining (Springer), IEEE Transactions on Services Computing, Human Computation, International Journal of Cooperative Information Systems, GeoJournal (Springer).
NSF Panelist: Fall 2009, Spring 2010, Spring 2011, Spring 2012, Fall 2013, Fall 2015, Spring 2016, Spring 2017.

Awards

SIGIR Test of Time Award Honorable Mention, 2022
College of Engineering Excellence Award for Oustanding Contributions, 2022
Lynn '84 and Bill Crane '83 Faculty Fellow, 2019-2021
Amazon Research Award, 2020
Presidential Impact Fellow, 2020
CIKM Test of Time Award, 2020
Dean of Engineering Excellence Award, 2020
Lynn '84 and Bill Crane '83 Faculty Fellow, 2019
WSDM 2019 Outstanding Senior Program Committee, 2019
Texas A&M Computer Science Undergraduate Faculty Teaching Award, 2019
TEES Faculty Fellow Award, 2018-2019
Dean of Engineering Excellence Award, 2016-2017
Holleran-Bowman Faculty Fellow, 2016-2017
Google Research Award, 2014
College of Engineering Caterpillar Teaching Excellence Award, 2013-2014
Texas A&M Computer Science Undergraduate Faculty Teaching Award, 2013
NSF CAREER Award, 2012 [Project Page]
Air Force Office of Scientific Research (AFOSR) Young Investigator Award, 2012
Texas A&M Center for Teaching Excellence Montague-CTE Scholar, 2011-2012
Defense Advanced Research Projects Agency (DARPA) Young Faculty Award, 2010
Texas A&M Computer Science Graduate Faculty Teaching Award, 2010
Google Research Award, 2010
Texas A&M Computer Science Graduate Faculty Teaching Award, 2009
Google Research Award, 2008