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- Professor
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- Websites
Caverlee, James Professor - Term Appoint
Positions
- Professor - Term Appoint, Computer Science and Engineering, College of Engineering
My research is generally in the areas of web-scale information management, distributed data-intensive
systems, and social computing. Fundamentally, my overall research goal is to develop algorithms and
systems to enable efficient and trustworthy information sharing and knowledge discovery over dynamic,
heterogeneous, and massive-scale networked information systems.
systems, and social computing. Fundamentally, my overall research goal is to develop algorithms and
systems to enable efficient and trustworthy information sharing and knowledge discovery over dynamic,
heterogeneous, and massive-scale networked information systems.
- Overview
- Publications
- Research
- Teaching
- Works By Students
- Background
- Contact
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Overview
Publications
selected publications
-
academic article
-
Liu, W., Zhang, Y., Wang, J., He, Y., Caverlee, J., Chan, P., Yeung, D. S., & Heng, P.
(2022).
Item Relationship Graph Neural Networks for E-Commerce. 33(9), 4785-4799.
-
Ding, K., Wang, J., Caverlee, J., & Liu, H.
(2022).
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning. 36(6), 6524-6531.
-
Wang, J., Ding, K., & Caverlee, J.
(2021).
Sequential Recommendation for Cold-start Users with Meta Transitional Learning. abs/2107.06427, 1783-1787.
-
Ding, K., Wang, J., Li, J., Caverlee, J., & Liu, H.
(2021).
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification. abs/2106.06873.
-
Wu, J., Nivargi, R., Lanka, S., Menon, A. M., Modukuri, S. A., Nakshatri, N., ... Giles, C. L.
(2021).
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models. abs/2104.04580.
-
Zhu, Z., Wang, J., & Caverlee, J.
(2021).
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning. abs/2103.07849.
-
Daryani, M., & Caverlee, J.
(2021).
Identifying Hijacked Reviews. abs/2107.05385, 70-78.
-
Hooshmand, H., & Caverlee, J.
(2020).
Understanding Car-Speak: Replacing Humans in Dealerships. abs/2002.02070.
-
He, Y., Zhang, Y., Liu, W., & Caverlee, J.
(2020).
Consistency-Aware Recommendation for User-Generated Item List Continuation. abs/1912.13031, 250-258.
-
He, Y., Zhu, Z., Zhang, Y., Chen, Q., & Caverlee, J.
(2020).
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. abs/2010.03746, 4604-4614.
-
He, Y., Wang, Z., Zhang, Y., Huang, R., & Caverlee, J.
(2020).
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge. abs/2010.03725, 7572-7582.
-
Song, Q., Ge, H., Caverlee, J., & Hu, X.
(2019).
Tensor Completion Algorithms in Big Data Analytics. ACM Transactions on Knowledge Discovery from Data.
13(1), 6-48.
-
He, Y., Wang, J., Niu, W., & Caverlee, J.
(2019).
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists. abs/1912.13023.
-
He, Y., Chen, H., Zhu, Z., & Caverlee, J.
(2019).
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation. abs/1901.00597.
-
Song, Q., Ge, H., Caverlee, J., & Hu, X.
(2019).
Tensor Completion Algorithms in Big Data Analytics. 13, 6:1-6:1.
-
Knott, B. A., Gratch, J., Cangelosi, A., & Caverlee, J.
(2018).
ACM Transactions on Interactive Intelligent Systems (TiiS) Special Issue on Trust and Influence in Intelligent Human-Machine Interaction. The ACM Transactions on Interactive Intelligent Systems.
8(4), 25-3.
-
He, Y., Chen, H., Zhu, Z., & Caverlee, J.
(2018).
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
2018-November, 1025-1030.
-
Zhu, Z., Wang, J., Zhang, Y., & Caverlee, J.
(2018).
Fairness-Aware Recommendation of Information Curators. abs/1809.03040.
-
Knott, B. A., Gratch, J., Cangelosi, A., & Caverlee, J.
(2018).
ACM Transactions on Interactive Intelligent Systems (TiiS) Special Issue on Trust and Influence in Intelligent Human-Machine Interaction. 8, 25:1-25:1.
-
Bahl, V., Carminati, B., Caverlee, J., Chen, I., Hsu, W., Ishida, T., ... Shyu, M.
(2018).
Editorial. IEEE Transactions on Services Computing.
11(1), 1-4.
-
Yao, W., Dai, Z., Huang, R., & Caverlee, J.
(2017).
Online deception detection refueled by real world data collection. International Conference Recent Advances in Natural Language Processing, RANLP.
2017-September, 793-802.
-
Niu, W., Liu, Z., & Caverlee, J.
(2016).
On Local Expert Discovery via Geo-Located Crowds, Queries, and Candidates. ACM Transactions on Spatial Algorithms and Systems.
2(4), 14-24.
-
Rudesill, D. S., Caverlee, J., & Sui, D.
(2015).
The Deep Web and the Darknet: A Look Inside the Internet's Massive Black Box
-
Jatowt, A., Castillo, C., Caverlee, J., & Tanaka, K.
(2014).
Report on the WebQuality 2014 Workshop. Sigir Forum.
48(2), 93-95.
-
Bertino, E., Caverlee, J., & Ferrari, E.
(2014).
Identity, Privacy, and Deception in Social Networks. IEEE Internet Computing.
18(2), 7-9.
-
Bertino, E., Caverlee, J., & Ferrari, E.
(2014).
Identity, Privacy, and Deception in Social Networks Introduction. IEEE Internet Computing.
18(2), 7-9.
-
Lee, K., Caverlee, J., Cheng, Z., & Sui, D. Z.
(2013).
Campaign Extraction from Social Media. ACM Transactions on Intelligent Systems and Technology.
5(1), 1-28.
-
Caverlee, J., Pu, C., Georgakopoulos, D., & Joshi, J.
(2013).
Editorial for CollaborateCom 2011 Special Issue. Mobile Networks and Applications.
18(2), 235-236.
-
Caverlee, J., Cheng, Z., Sui, D. Z., & Kamath, K. Y.
(2013).
Towards Geo-Social Intelligence: Mining, Analyzing, and Leveraging Geospatial Footprints in Social Media. 36(3), 33-41.
-
Cheng, Z., Caverlee, J., & Lee, K.
(2013).
A Content-Driven Framework for Geolocating Microblog Users. ACM Transactions on Intelligent Systems and Technology.
4(1), 2-27.
-
Hsu, C., Caverlee, J., & Khabiri, E.
(2012).
Predicting community preference of comments on the Social Web. Web Intelligence and Agent Systems: An International Journal.
10(4), 447-463.
-
Kashoob, S., & Caverlee, J.
(2012).
Temporal dynamics of communities in social bookmarking systems. Social Network Analysis and Mining.
2(4), 387-404.
-
Ding, Y., Yan, E., Frazho, A. R., & Caverlee, J.
(2010).
PageRank for ranking authors in co-citation networks. abs/1012.4872(11), 2229-2243.
-
Caverlee, J., Liu, L., & Webb, S.
(2010).
The SocialTrust framework for trusted social information management: Architecture and algorithms. 180(1), 95-112.
-
Ding, Y., Jacob, E. K., Caverlee, J., Fried, M., & Zhang, Z.
(2009).
Profiling Social Networks: A Social Tagging Perspective. D-Lib Magazine.
15(3/4),
-
Caverlee, J., Webb, S., Liu, L., & Rouse, W. B.
(2009).
A Parameterized Approach to Spam-Resilient Link Analysis of the Web. IEEE Transactions on Parallel and Distributed Systems.
20(10), 1422-1438.
-
Ding, Y., Yan, E., Frazho, A., & Caverlee, J.
(2009).
PageRank for Ranking Authors in Co-citation Networks. 60(11), 2229-2243.
-
Bae, J., Liu, L., Caverlee, J., Zhang, L., & Bae, H.
(2007).
Development of distance measures for process mining, discovery, and integration. International Journal of Web Services Research.
4(4), 1-17.
-
Rocco, D., Caverlee, J., Liu, L., & Critchlow, T.
(2007).
Service class driven dynamic data source discovery with DynaBot. International Journal of Web Services Research.
4(3), 26-48.
-
Caverlee, J., Bae, J., Wu, Q., Liu, L., Pu, C., & Rouse, W. B.
(2007).
Workflow management for enterprise transformation. 6(1-2), 61-80.
-
Liu, L., Zhang, J., Han, W., Pu, C., Caverlee, J., Park, S., ... Coleman, M.
(2006).
XWRAPComposer: A multi-page data extraction service. International Journal of Web Services Research.
3(2), 33-60.
-
Liu, L., Buttler, D., Caverlee, J., Pu, C., & Zhang, J.
(2005).
A methodical approach to extracting interesting objects from dynamic web pages. International Journal of Web and Grid Services.
1(2), 165-165.
-
Liu, W., Zhang, Y., Wang, J., He, Y., Caverlee, J., Chan, P., Yeung, D. S., & Heng, P.
(2022).
Item Relationship Graph Neural Networks for E-Commerce. 33(9), 4785-4799.
-
chapter
-
Zhang, H., Zhu, Z., & Caverlee, J.
(2023).
Evolution of Filter Bubbles and Polarization in News Recommendation. Lecture Notes in Artificial Intelligence.
Advances in Information Retrieval.
(pp. 685-693).
Springer Nature.
-
Wang, D. e., Webb, S., Lee, K., Caverlee, J., & Pu, C.
(2021).
Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities. Encyclopedia of Complexity and Systems Science.
(pp. 1-12).
Springer Nature.
-
Caverlee, J.
(2018).
Data Dictionary. Liu, L., & Özsu, M. T. (Eds.),
Encyclopedia of Database Systems.
(pp. 749-750).
Springer Nature.
-
Caverlee, J., Mitra, P., & Larsgaard, M. L.
(2018).
Dublin Core. Encyclopedia of Database Systems.
(pp. 1248-1251).
Springer Nature.
-
Caverlee, J., & Lee, K.
(2015).
Weaponized Crowdsourcing: An Emerging Threat and Potential Countermeasures. TRANSPARENCY IN SOCIAL MEDIA: TOOLS, METHODS AND ALGORITHMS FOR MEDIATING ONLINE INTERACTIONS.
(pp. 51-65).
Springer Nature.
-
Caverlee, J., & Cheng, Z.
(2014).
Geography and Web Communities. Encyclopedia of Social Network Analysis and Mining.
(pp. 609-611).
Springer Nature.
-
Caverlee, J.
(2013).
Exploitation in Human Computation Systems. Michelucci, P. (Eds.),
Handbook of Human Computation.
(pp. 837-845).
Springer Nature.
-
Webb, S., Caverlee, J., & Pu, C.
(2012).
Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities. Computational Complexity.
(pp. 1433-1443).
Springer Nature.
-
Caverlee, J.
(2009).
Data Dictionary. Liu, L., & Özsu, M. T. (Eds.),
Encyclopedia of Database Systems.
(pp. 570-571).
Springer Nature.
-
Caverlee, J., Mitra, P., & Laarsgard, M.
(2009).
Dublin Core. Liu, L., & Özsu, M. T. (Eds.),
Encyclopedia of Database Systems.
(pp. 949-949).
Springer Nature.
-
Webb, S., Caverlee, J., & Pu, C.
(2009).
Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities. Meyers, R. A. (Eds.),
Encyclopedia of Complexity and Systems Science.
(pp. 4367-4378).
Springer Nature.
-
Caverlee, J.
(2009).
Topic Maps. Liu, L., & Özsu, M. T. (Eds.),
Encyclopedia of Database Systems.
(pp. 3124-3126).
Springer Nature.
-
Liu, L., Zhang, J., Han, W., Pu, C., Caverlee, J., Park, S., & Critchlow, T.
(2008).
XWRAPComposer. Advances in Web Services Research.
(pp. 189-223).
IGI Global.
-
Sugumaran, V., Liu, L., Zhang, J., Han, W., Pu, C., Caverlee, J., ... Coleman, M.
(2008).
XWRAPComposer. Intelligent Information Technologies.
(pp. 1147-1173).
IGI Global.
-
Zhang, H., Zhu, Z., & Caverlee, J.
(2023).
Evolution of Filter Bubbles and Polarization in News Recommendation. Lecture Notes in Artificial Intelligence.
Advances in Information Retrieval.
(pp. 685-693).
Springer Nature.
-
conference paper
-
Lin, A., Wang, J., Zhu, Z., & Caverlee, J.
(2022).
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems. 1238-1247.
-
Zhu, Z., He, Y., Zhao, X., & Caverlee, J.
(2021).
Popularity Bias in Dynamic Recommendation. 2439-2449.
-
Zhu, Z., Kim, J., Nguyen, T., Fenton, A., & Caverlee, J.
(2021).
Fairness among New Items in Cold Start Recommender Systems. 767-776.
-
Daryani, M., & Caverlee, J.
(2021).
Identifying Hijacked Reviews. 70-78.
-
Zhao, X., Zhu, Z., & Caverlee, J.
(2021).
Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests. 888-899.
-
Madanagopal, K., & Caverlee, J.
(2021).
Towards Ongoing Detection of Linguistic Bias on Wikipedia. 629-631.
-
Zhu, Z., He, Y., Zhao, X., Zhang, Y., Wang, J., & Caverlee, J.
(2021).
Popularity-Opportunity Bias in Collaborative Filtering. 85-93.
-
Wang, J., Ding, K., & Caverlee, J.
(2021).
Sequential Recommendation for Cold-start Users with Meta Transitional Learning. 1783-1787.
-
Wang, J., Ding, K., Zhu, Z., & Caverlee, J.
(2021).
Session-based Recommendation with Hypergraph Attention Networks. 82-90.
-
Zhang, Y., Zhu, Z., He, Y., & Caverlee, J.
(2020).
Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. 43-52.
-
Zhu, Z., He, Y., Zhang, Y., & Caverlee, J.
(2020).
Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. 551-556.
-
Wang, J., Ding, K., Hong, L., Liu, H., & Caverlee, J.
(2020).
Next-item Recommendation with Sequential Hypergraphs. 1101-1110.
-
Zhu, Z., Sefati, S., Saadatpanah, P., & Caverlee, J.
(2020).
Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. 1121-1130.
-
Zhang, Y., He, Y., Wang, J., & Caverlee, J.
(2020).
Adaptive Hierarchical Translation-based Sequential Recommendation. 2984-2990.
-
Zhao, X., Zhu, Z., Alfifi, M., & Caverlee, J.
(2020).
Addressing the Target Customer Distortion Problem in Recommender Systems. 2969-2975.
-
Wang, J., & Caverlee, J.
(2020).
Recommending Music Curators: A Neural Style-Aware Approach. Lecture Notes in Artificial Intelligence.
191-204.
-
Zhao, X., Zhu, Z., Zhang, Y., & Caverlee, J.
(2020).
Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations. 762-770.
-
Wang, J., Zhu, Z., & Caverlee, J.
(2020).
User Recommendation in Content Curation Platforms. 627-635.
-
Chen, J., Mao, J., Liu, Y., Zhang, M., & Ma, S.
(2020).
A Context-Aware Click Model for Web Search. 88-96.
-
He, Y., Zhang, Y., Liu, W., & Caverlee, J.
(2020).
Consistency-Aware Recommendation for User-Generated Item List Continuation. 250-258.
-
Wang, J., Ding, K., Zhu, Z., Zhang, Y., & Caverlee, J.
(2020).
Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion. 636-644.
-
Wang, J., Louca, R., Hu, D., Cellier, C., Caverlee, J., & Hong, L.
(2020).
Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce. 645-653.
-
Kaghazgaran, P., & Caverlee, J.
(2020).
Towards an Automated Writing Assistant for Online Reviews
-
He, Y., Zhu, Z., Zhang, Y., Chen, Q., & Caverlee, J.
(2020).
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. 4604-4614.
-
Wang, J., Ding, K., Zhu, Z., Zhang, Y., & Caverlee, J.
(2020).
Key Opinion Leaders in Recommendation Systems. 636-644.
-
He, Y., Wang, Z., Zhang, Y., Huang, R., & Caverlee, J.
(2020).
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge. 7572-7582.
-
Wang, J., Louca, R., Hu, D., Cellier, C., Caverlee, J., & Hong, L.
(2020).
Time to Shop for Valentine's Day. 645-653.
-
Caverlee, J. B.
(2020).
WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020
-
He, Y., Wang, J., Niu, W., & Caverlee, J.
(2019).
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists. 1481-1490.
-
Zhang, Y., & Caverlee, J.
(2019).
Instagrammers, Fashionistas, and Me. 1583-1592.
-
Kaghazgaran, P., Alfifi, M., & Caverlee, J.
(2019).
Wide-Ranging Review Manipulation Attacks. 981-990.
-
Zhu, Z., Wang, J., & Caverlee, J.
(2019).
Improving Top-K Recommendation via Joint Collaborative Autoencoders. 3483-3489.
-
Zhang, Y., & Caverlee, J.
(2019).
Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence. 1583-1592.
-
Kaghazgaran, P., Alfifi, M., & Caverlee, J.
(2019).
Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures. 981-990.
-
Alfifi, M., Kaghazgaran, P., Caverlee, J., & Morstatter, F.
(2019).
A Large-Scale Study of ISIS Social Media Strategy: Community Size, Collective Influence, and Behavioral Impact. 58-67.
-
Zhang, Y., Liu, N., Ji, S., Caverlee, J., & Hu, X.
(2019).
An Interpretable Neural Model with Interactive Stepwise Influence. Lecture Notes in Artificial Intelligence.
528-540.
-
Wang, J., & Caverlee, J.
(2019).
Recurrent Recommendation with Local Coherence. 564-572.
-
Kaghazgaran, P., Alfifi, M., & Caverlee, J.
(2019).
TOmCAT: Target-Oriented Crowd Review Attacks and Countermeasures. 302-312.
-
Zhao, X., Song, Q., Caverlee, J., & Hu, X.
(2018).
TrailMix: An Ensemble Recommender System for Playlist Curation and Continuation. 1-6.
-
Cao, C., Chen, Z., Caverlee, J., Tang, L., Luo, C., & Li, Z.
(2018).
Behavior-based Community Detection: Application to Host Assessment In Enterprise Information Networks. 1977-1985.
-
Zhu, Z., Hu, X., & Caverlee, J.
(2018).
Fairness-Aware Tensor-Based Recommendation. 1153-1162.
-
Zhang, Y., Lu, H., Niu, W., & Caverlee, J.
(2018).
Quality-Aware Neural Complementary Item Recommendation. 77-85.
-
Lu, H., Niu, W., & Caverlee, J.
(2018).
Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User Factorization. 205-214.
-
Ge, H., Zhang, K., Alfifi, M., Hu, X., & Caverlee, J.
(2018).
DisTenC: A Distributed Algorithm for Scalable Tensor Completion on Spark. Proceedings / International Conference on Data Engineering. International Conference on Data Engineering.
137-148.
-
Kaghazgaran, P., Caverlee, J., & Squicciarini, A.
(2018).
Combating Crowdsourced Review Manipulators: A Neighborhood-Based Approach. 306-314.
-
Niu, W., Caverlee, J., & Lu, H.
(2018).
Neural Personalized Ranking for Image Recommendation. 423-431.
-
Qiu, C., Squicciarini, A., Khare, D. R., Carminati, B., & Caverlee, J.
(2018).
CrowdEval: A Cost-Efficient Strategy to Evaluate Crowdsourced Worker's Reliability. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS.
1486-1494.
-
Niu, W., Caverlee, J., & Lu, H.
(2018).
Location-Sensitive User Profiling Using Crowdsourced Labels. Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.
386-393.
-
Zhao, X., & Caverlee, J.
(2018).
Vitriol on Social Media: Curation and Investigation. Lecture Notes in Artificial Intelligence.
487-504.
-
Yao, W., Dai, Z., Huang, R., & Caverlee, J.
(2017).
Online Deception Detection Refueled by RealWorld Data Collection. 793-802.
-
Li, S., Caverlee, J., Niu, W., & Kaghazgaran, P.
(2017).
Crowdsourced App Review Manipulation. 1137-1140.
-
Song, Q., Huang, X., Ge, H., Caverlee, J., & Hu, X.
(2017).
Multi-Aspect Streaming Tensor Completion. 435-443.
-
Cao, C., Ge, H., Lu, H., Hu, X., & Caverlee, J.
(2017).
What Are You Known For? Learning User Topical Profiles with Implicit and Explicit Footprints. 743-752.
-
Qiu, C., Squicciarini, A. C., Rajtmajer, S. M., & Caverlee, J.
(2017).
Dynamic Contract Design for Heterogenous Workers in Crowdsourcing for Quality Control. International Conference on Distributed Computing Systems.
1168-1177.
-
Kaghazgaran, P., Caverlee, J., & Alfifi, M.
(2017).
Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond. 560-563.
-
Qiu, C., Squicciarini, A. C., Carminati, B., Caverlee, J., & Khare, D. R.
(2016).
CrowdSelect: Increasing Accuracy of Crowdsourcing Tasks through Behavior Prediction and User Selection. 539-548.
-
Ge, H., Caverlee, J., Zhang, N., & Squicciarini, A.
(2016).
Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information. 1493-1502.
-
Lu, H., Caverlee, J., & Niu, W.
(2016).
Discovering What You're Known For: A Contextual Poisson Factorization Approach. 253-260.
-
Ge, H., Caverlee, J., & Lu, H.
(2016).
TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation. 261-268.
-
Niu, W., Caverlee, J., Lu, H., & Kamath, K.
(2016).
Community-Based Geospatial Tag Estimation. 279-286.
-
Caverlee, J. B.
(2016).
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, CA, USA, August 18-21, 2016
-
Caverlee, J., Kumar, R., & Tong, H.
(2016).
Welcome from the ASONAM 2016 program chairs. xxii-xxii.
-
Ge, H., & Caverlee, J.
(2016).
College Towns, Vacation Spots, and Tech Hubs: Using Geo-Social Media to Model and Compare Locations. Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.
129-136.
-
Niu, W., Liu, Z., & Caverlee, J.
(2016).
LExL: A Learning Approach for Local Expert Discovery on Twitter. 803-809.
-
Lu, H., Caverlee, J., & Niu, W.
(2015).
BiasWatch. 213-222.
-
Cao, C., Caverlee, J., Lee, K., Ge, H., & Chung, J.
(2015).
Organic or Organized?. 513-522.
-
Lu, H., & Caverlee, J.
(2015).
Exploiting Geo-Spatial Preference for Personalized Expert Recommendation. 67-74.
-
Berberich, K., Caverlee, J., Efron, M., Hauff, C., Murdock, V., Shokouhi, M., & Thomee, B.
(2015).
SIGIR 2015 Workshop on Temporal, Social and Spatially-aware Information Access (#TAIA2015). 1149-1150.
-
Fayazi, A., Lee, K., Caverlee, J., & Squicciarini, A.
(2015).
Uncovering Crowdsourced Manipulation of Online Reviews. 233-242.
-
Liang, Y., Caverlee, J., & Cao, C.
(2015).
A Noise-Filtering Approach for Spatio-temporal Event Detection in Social Media. Lecture Notes in Artificial Intelligence.
233-244.
-
Modani, N., Khabiri, E., Srinivasan, H., & Caverlee, J.
(2015).
Creating Diverse Product Review Summaries: A Graph Approach. Lecture Notes in Artificial Intelligence.
169-184.
-
Ge, H., Caverlee, J., & Lee, K.
(2015).
Crowds, Gigs, and Super Sellers: A Measurement Study of a Supply-Driven Crowdsourcing Marketplace. 120-129.
-
Cao, C., & Caverlee, J.
(2015).
Detecting Spam URLs in Social Media via Behavioral Analysis. Lecture Notes in Artificial Intelligence.
703-714.
-
Mahmud, J., Nichols, J., Zhou, M., Caverlee, J., Zeng, Y. i., Chen, L., & O'Donovan, J.
(2014).
DUBMOD14 - International Workshop on Data-driven User Behavioral Modeling and Mining from Social Media. 2092-2093.
-
Cao, C., & Caverlee, J.
(2014).
Behavioral Detection of Spam URL Sharing: Posting Patterns versus Click Patterns. 138-141.
-
Cheng, Z., Caverlee, J., Barthwal, H., & Bachani, V.
(2014).
Who is the Barbecue King of Texas?: A Geo-Spatial Approach to Finding Local Experts on Twitter. 334-344.
-
Cheng, Z., Caverlee, J., Barthwal, H., & Bachani, V.
(2014).
Finding Local Experts on Twitter. 241-242.
-
Lee, K., Caverlee, J., & Pu, C.
(2014).
Social Spam, Campaigns, Misinformation and Crowdturfing. 199-199.
-
Caverlee, J. B.
(2014).
Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMOD@CIKM 2014, Shanghai, China, November 3, 2014
-
Mahmud, J., Caverlee, J., Nichols, J., O'Donovan, J., Zhou, M., Zeng, Y., & Chen, L.
(2014).
DUBMOD 2014 Chairs' welcome. iii.
-
Mahmud, J., Caverlee, J., Nichols, J., O'Donovan, J., & Zhou, M.
(2013).
DUBMOD 2013 chairs' welcome
-
Mahmud, J., Nichols, J., Zhou, M., Caverlee, J., & O'Donovan, J.
(2013).
DUBMOD ''13-International Workshop on Data-driven User Behavioral Modelling and Mining from Social Media. 2551-2552.
-
McGee, J., Caverlee, J., & Cheng, Z.
(2013).
Location Prediction in Social Media Based on Tie Strength. 459-468.
-
Kamath, K. Y., & Caverlee, J.
(2013).
Spatio-Temporal Meme Prediction: Learning What Hashtags Will Be Popular Where. 1341-1350.
-
Kamath, K. Y., Popescu, A., & Caverlee, J.
(2013).
Board Coherence in Pinterest: Non-visual Aspects of a Visual Site. 49-50.
-
Liang, Y., Caverlee, J., Cheng, Z., & Kamath, K. Y.
(2013).
How big is the crowd?. 99-108.
-
Caverlee, J. B.
(2013).
Proceedings of the 2013 Workshop on Data-Driven User Behavioral Modelling and Mining from Social Media, DUBMOD@CIKM 2013 San Francisco, CA, USA, October 28, 2013
-
Kamath, K. Y., Caverlee, J., Lee, K., & Cheng, Z.
(2013).
Spatio-temporal dynamics of online memes: a study of geo-tagged tweets. 667-678.
-
Liang, Y., Caverlee, J., & Mander, J.
(2013).
Text vs. Images: On the Viability of Social Media to Assess Earthquake Damage. 1003-1006.
-
Kamath, K. Y., Popescu, A. M., & Caverlee, J.
(2013).
Board recommendation in Pinterest. CEUR Workshop Proceedings.
-
Lee, K., Kamath, K., & Caverlee, J.
(2013).
Combating Threats to Collective Attention in Social Media: An Evaluation. 321-330.
-
Lee, K., Tamilarasan, P., & Caverlee, J.
(2013).
Crowdturfers, Campaigns, and Social Media: Tracking and Revealing Crowdsourced Manipulation of Social Media. 331-340.
-
Popescu, A. M., Kamath, K. Y., & Caverlee, J.
(2013).
Mining potential domain expertise in Pinterest. CEUR Workshop Proceedings.
-
Mahmud, J., Caverlee, J., Nichols, J., O'Donovan, J., & Zhou, M.
(2012).
DUBMMSM 2012 chairs' welcome
-
Lee, K., Caverlee, J., Kamath, K. Y., & Cheng, Z.
(2012).
Detecting collective attention spam. 48-55.
-
Kamath, K. Y., & Caverlee, J.
(2012).
Content-based crowd retrieval on the real-time web. 195-204.
-
Mahmud, J., Caverlee, J., Nichols, J., Donovan, J. O., & Zhou, M.
(2012).
DUBMMSM'12. 2752-2753.
-
Khabiri, E., Caverlee, J., & Kamath, K. Y.
(2012).
Predicting semantic annotations on the real-time web. 219-228.
-
Caverlee, J. B.
(2012).
Proceedings of the 2012 workshop on Data-driven User Behavioral Modelling and Mining from Social Media, DUBMMSM 2012, October 29, 2012, Maui, Hawaii, USA
-
Caverlee, J., Cheng, Z., Yee, W. G., Liew, R., & Liang, Y.
(2012).
Public checkins versus private queries: measuring and evaluating spatial preference. 40-47.
-
Kamath, K. Y., Caverlee, J., Cheng, Z., & Sui, D. Z.
(2012).
Spatial influence vs. community influence. 962-971.
-
Kamath, K. Y., & Caverlee, J.
(2011).
Expert-Driven Topical Classification of Short Message Streams. 388-393.
-
McGee, J., Caverlee, J. A., & Cheng, Z.
(2011).
A geographic study of tie strength in social media. 2333-2336.
-
Lee, K., Caverlee, J., Cheng, Z., & Sui, D. Z.
(2011).
Content-driven detection of campaigns in social media. 551-556.
-
Caverlee, J., Cheng, Z., Eoff, B., Hsu, C., Kamath, K., & McGee, J.
(2011).
CrowdTracker: Enabling Community-Based Real-Time Web Monitoring. 1283-1283.
-
Kamath, K. Y., & Caverlee, J.
(2011).
Discovering trending phrases on information streams. 2245-2248.
-
Hsu, C., Caverlee, J., & Khabiri, E.
(2011).
Hierarchical comments-based clustering. 1130-1137.
-
Cheng, Z., Caverlee, J., Kamath, K. Y., & Lee, K.
(2011).
Toward traffic-driven location-based web search. 805-814.
-
Kamath, K. Y., & Caverlee, J.
(2011).
Transient crowd discovery on the real-time social web. 585-594.
-
Cheng, Z., Caverlee, J., Lee, K., & Sui, D.
(2011).
Exploring Millions of Footprints in Location Sharing Services. 81-88.
-
Lee, K., Eoff, B., & Caverlee, J.
(2011).
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter. 185-192.
-
Khabiri, E., Caverlee, J., & Hsu, C.
(2011).
Summarizing User-Contributed Comments. 534-537.
-
Lee, K., Eoff, B., & Caverlee, J.
(2010).
Devils, Angels, and Robots: Tempting Destructive Users in Social Media. 275-278.
-
Webb, S., Caverlee, J., & Pu, C.
(2010).
A Summary of Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities. 39-40.
-
Kashoob, S., Caverlee, J., & Kamath, K.
(2010).
Community-based ranking of the social web. 141-150.
-
Kamath, K. Y., & Caverlee, J.
(2010).
Identifying hotspots on the real-time web. 1837-1840.
-
Lee, K., Caverlee, J., & Webb, S.
(2010).
The social honeypot project: protecting online communities from spammers. 1139-1140.
-
Lee, K., Caverlee, J., & Webb, S.
(2010).
Uncovering Social Spammers: Social Honeypots plus Machine Learning. 435-442.
-
Cheng, Z., Caverlee, J., & Lee, K.
(2010).
You are where you tweet. 759-768.
-
Caverlee, J., Cheng, Z., Eoff, B., Hsu, C., Kamath, K., Kashoob, S., ... Lee, K.
(2010).
SocialTrust++: Building community-based trust in Social Information Systems. 1-7.
-
Kashoob, S., Caverlee, J., & Ding, Y.
(2009).
A Categorical Model for Discovering Latent Structure in Social Annotations. 82-89.
-
Khabiri, E., Hsu, C., & Caverlee, J.
(2009).
Analyzing and Predicting Community Preference of Socially Generated Metadata: A Case Study on Comments in the Digg Community. 238-241.
-
Kashoob, S., Caverlee, J., & Khabiri, E.
(2009).
Probabilistic Generative Models of the Social Annotation Process. 42-49.
-
Hsu, C., Khabiri, E., & Caverlee, J.
(2009).
Ranking Comments on the Social Web. 90-97.
-
Caverlee, J.
(2008).
A Large-Scale Study of MySpace: Observations and Implications for Online Social Networks. 36-44.
-
Graham, R., & Caverlee, J.
(2008).
Exploring Feedback Models in Interactive Tagging. 141-147.
-
Caverlee, J., Liu, L., & Webb, S.
(2008).
Socialtrust: tamper-resilient trust establishment in online communities. 104-114.
-
Graham, R., Eoff, B., & Caverlee, J.
(2008).
Plurality: a context-aware personalized tagging system. 1165-1166.
-
Webb, S., Caverlee, J., & Pu, C.
(2008).
Predicting web spam with HTTP session information. 339-348.
-
Caverlee, J., Liu, L., & Webb, S.
(2008).
Towards robust trust establishment in web-based social networks with socialtrust. 1163-1164.
-
Webb, S., Caverlee, J., & Pu, C.
(2008).
Social honeypots: Making friends with a spammer near you
-
Caverlee, J., Webb, S., & Liu, L.
(2007).
Spam-Resilient Web Rankings via Influence Throttling. 1-10.
-
Caverlee, J., & Liu, L.
(2007).
Countering Web Spam with Credibility-Based Link Analysis. 157-166.
-
Bamba, B., Liu, L., Caverlee, J., Padliya, V., Srivatsa, M., Bansal, T., ... Singh, A.
(2007).
DSphere: A Source-Centric Approach to Crawling, Indexing and Searching the World Wide Web. Proceedings of the International Conference on Data Engineering.
1515-1516.
-
Bamba, B., Liu, L., Caverlee, J., Padliya, V., Srivatsa, M., Bansal, T., ... Singh, A.
(2007).
DSphere: A source-centric approach to crawling, indexing and searching the world wide web. Proceedings / International Conference on Data Engineering. International Conference on Data Engineering.
1490-+.
-
Webb, S., Caverlee, J., & Pu, C.
(2007).
Characterizing web spam using content and HTTP session analysis
-
Caverlee, J., Liu, L., & Rocco, D.
(2006).
Discovering interesting relationships among deep web databases: A source-biased approach. World Wide Web.
585-622.
-
Caverlee, J., Liu, L., & Rouse, W. B.
(2006).
Link-based ranking of the web with source-centric collaboration. 61-+.
-
Bae, J., Liu, L., Caverlee, J., & Rouse, W. B.
(2006).
Process mining, discovery, and integration using distance measures. 479-486.
-
Caverlee, J., Liu, L., & Bae, J.
(2006).
Distributed query sampling. 340-347.
-
Webb, S., Caverlee, J., & Pu, C.
(2006).
Introducing the Webb Spam Corpus: Using email spam to identify Web spam automatically
-
Bae, J., Caverlee, J., Liu, L., & Yan, H.
(2006).
Process mining by measuring process block similarity. Lecture Notes in Artificial Intelligence.
141-152.
-
Rocco, D., Caverlee, J., & Liu, L.
(2005).
XPack: A high-performance Web document encoding. 32-39.
-
Caverlee, J., & Liu, L.
(2005).
QA-Pagelet: Data preparation techniques for large-scale data analysis of the Deep Web. IEEE Transactions on Knowledge and Data Engineering.
1247-1262.
-
Rocco, D., Caverlee, J., Liu, L., & Critchlow, T.
(2005).
Domain-specific web service discovery with service class descriptions. 481-488.
-
Rocco, D., Caverlee, J., Liu, L., & Critchlow, T.
(2005).
Exploiting the deep web with DynaBot: matching, probing, and ranking. 1174-1175.
-
Liu, L., Zhang, J. J., Han, W., Pu, C., Caverlee, J., Park, S., ... Buttler, D.
(2005).
XWRAPComposer: A multi-page data extraction service for bio-computing applications. 271-278.
-
Caverlee, J., Liu, L., & Rocco, D.
(2004).
Discovering and ranking web services with BASIL: a personalized approach with biased focus. 153-162.
-
Caverlee, J., Liu, L., & Buttler, D.
(2004).
Probe, cluster, and discover: Focused extraction of QA-Pagelets from the Deep Web. Proceedings / International Conference on Data Engineering. International Conference on Data Engineering.
103-114.
-
Lin, A., Wang, J., Zhu, Z., & Caverlee, J.
(2022).
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems. 1238-1247.
-
institutional repository document
-
Lin, A., Zhu, Z., Wang, J., & Caverlee, J.
(2023).
Enhancing User Personalization in Conversational Recommenders
-
Zhang, H., Zhu, Z., & Caverlee, J.
(2023).
Evolution of Filter Bubbles and Polarization in News Recommendation
-
Zhang, Y., Wang, R., Cheng, D. Z., Yao, T., Yi, X., Hong, L., Caverlee, J., & Chi, E. H.
(2022).
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
-
Dong, X., Lu, J., Wang, J., & Caverlee, J.
(2022).
Closed-book Question Generation via Contrastive Learning
-
Lin, A., Zhu, Z., Wang, J., & Caverlee, J.
(2022).
Towards Fair Conversational Recommender Systems
-
Lin, A., Wang, J., Zhu, Z., & Caverlee, J.
(2022).
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
-
Zhu, Z., He, Y., Zhao, X., & Caverlee, J.
(2022).
Evolution of Popularity Bias: Empirical Study and Debiasing
-
Wang, J., Ding, K., Zhu, Z., & Caverlee, J.
(2021).
Session-based Recommendation with Hypergraph Attention Networks
-
Ding, K., Wang, J., Caverlee, J., & Liu, H.
(2021).
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
-
Daryani, M., & Caverlee, J.
(2021).
Identifying Hijacked Reviews
-
Wang, J., Ding, K., & Caverlee, J.
(2021).
Sequential Recommendation for Cold-start Users with Meta Transitional Learning
-
Ding, K., Wang, J., Li, J., Caverlee, J., & Liu, H.
(2021).
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
-
Wu, J., Nivargi, R., Lanka, S., Menon, A. M., Modukuri, S. A., Nakshatri, N., ... Giles, C. L.
(2021).
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
-
Zhu, Z., Wang, J., & Caverlee, J.
(2021).
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
-
He, Y., Zhu, Z., Zhang, Y., Chen, Q., & Caverlee, J.
(2020).
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition
-
He, Y., Wang, Z., Zhang, Y., Huang, R., & Caverlee, J.
(2020).
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge
-
He, Y., Wang, J., Niu, W., & Caverlee, J.
(2019).
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists
-
He, Y., Zhang, Y., Liu, W., & Caverlee, J.
(2019).
Consistency-Aware Recommendation for User-Generated ItemList Continuation
-
He, Y., Chen, H., Zhu, Z., & Caverlee, J.
(2019).
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
-
Rudesill, D. S., Caverlee, J., & Sui, D.
(2015).
The Deep Web and the Darknet: A Look Inside the Internet's Massive Black Box
-
Ding, Y., Yan, E., Frazho, A., & Caverlee, J.
(2010).
PageRank for ranking authors in co-citation networks
-
Lin, A., Zhu, Z., Wang, J., & Caverlee, J.
(2023).
Enhancing User Personalization in Conversational Recommenders
Research
principal investigator on
- III: Small: Collaborative Research: Modeling and Managing Extremist Group Influence in Massive Social Media Networks awarded by National Science Foundation - (Arlington, Virginia, United States) 2019 - 2022
- EAGER: Fairness-Aware Personalized Recommendation awarded by National Science Foundation - (Arlington, Virginia, United States) 2018 - 2020
- HELIOS: Accelerated Recovery of Evolving Spatial-Temporal Dynamics awarded by United States Department of Defense - (Washington D.C., District of Columbia, United States) 2016 - 2020
- Collective Attention Threats: Models and Deterrence awarded by United States Air Force - (Arlington, Virginia, United States) 2015 - 2018
- FFATA: CAREER: Real-Time Crowd-Oriented Search and Computation Systems awarded by National Science Foundation - (Arlington, Virginia, United States) 2012 - 2018
- YIP12: Detecting, Analyzing, Modeling, and Predicting Strategic Manipulation and Adversarial Propaganda in Social Media awarded by United States Air Force - (Arlington, Virginia, United States) 2012 - 2015
co-principal investigator on
- FAI: Towards Fairness in Deep Neural Networks with Learning Interpretation awarded by National Science Foundation - (Arlington, Virginia, United States) 2020 - 2023
- SaTC: CORE: Small: Adversarial Learning via Modeling Interpretation awarded by National Science Foundation - (Arlington, Virginia, United States) 2018 - 2021
Teaching
teaching activities
- CSCE320 Principles Of Data Science Instructor
- CSCE470 Info Storage & Retrieval Instructor
- CSCE485 Directed Studies Instructor
- CSCE489 Sptp: Recsys Instructor
- CSCE489 Sptp:data Science & Analytics Instructor
- CSCE491 Hnr-research Instructor
- CSCE670 Info Storage & Retrieval Instructor
- CSCE676 Data Mining & Analysis Instructor
- CSCE684 Professional Internship Instructor
- CSCE684 Professional Internship: In-ab Instructor
- CSCE684 Professional Internship: In-ab Instructor
- CSCE685 Directed Studies Instructor
- CSCE691 Research Instructor
Works By Students
chaired theses and dissertations
-
Aggarwal, Anupam (2009-12). User Importance Modelling in Social Information Systems An Interaction Based Approach.
-
Bachani, Vandana (2013-12). Local Experts in Social Media.
-
Barthwal, Himanshu (2015-07). Location Sharing Behavior: Analysis and Models.
-
Cheng, Zhiyuan (2014-01). Toward Geo-social Information Systems: Methods and Algorithms.
-
Fayazi, Amir A (2013-11). Detecting Crowdsourced Spam Reviews in Social Media.
-
Hsu, Chiao-Fang (2009-12). Predicting Community Preference of Comments on the Social Web.
-
Ibrahim Abdelhalim, Ashraf A. (2016-01). Analysis of Twitter Hashtags' Geographic Propagation.
-
Kamath, Krishna Y (2013-05). Mining, Modeling, and Analyzing Real-Time Social Trails.
-
Kashoob, Said Masoud Ali (2011-05). Community-Oriented Models and Applications for the Social Web.
-
Khabiri, Elham (2013-05). Ranking, Labeling, and Summarizing Short Text in Social Media.
-
Lakshminarasimhan, Vardharaj (2014-12). A Latent Factor Model for Board Recommendations in Pinterest.
-
Lee, Kyumin (2013-06). Combating Threats to the Quality of Information in Social Systems.
-
Liang, Yuan (2013-07). Event Modeling in Social Media with Application to Disaster Damage Assessment.
-
Liu, Zhijiao (2015-12). A Learning Approach for Local Expert Discovery.
-
McGee, Jeffrey A (2013-05). Location Prediction in Social Media Based on Tie Strength.
-
Tamilarasan, Prithivi (2013-08). Combating Crowdsourced Manipulation of Social Media.
-
Venkatesh, Sindhuja (2014-12). Analysis of Local Experts in Social Media.
Background
education and training
- Ph.D. in Computer Science, Georgia Institute of Technology - (Atlanta, Georgia, United States) 2007
- M.S. in Engineering-Economic Systems and Operations Research, Stanford University - (Stanford, California, United States) 2001
- B.A. in Economics, Duke University - (Durham, North Carolina, United States) 1996
awards and honors
- Presidential Impact Fellows, conferred by Texas A&M University - (College Station, Texas, United States), 2020
- Faculty Early Career Development (CAREER) Program, conferred by National Science Foundation - (Arlington, Virginia, United States), 2012
- Montague Center for Teaching Excellence (CTE) Scholar, conferred by Texas A&M University - (College Station, Texas, United States), 2011
- Young Faculty Award, conferred by Defense Advanced Research Projects Agency - (Arlington, Virginia, United States), 2010
- CSE Graduate Faculty Teaching Excellence Award, conferred by Texas A&M University - (College Station, Texas, United States), 2010
- Faculty Research Award, conferred by Google - (Mountain View, California, United States), 2010
- CSE Graduate Faculty Teaching Excellence Award, conferred by Texas A&M University - (College Station, Texas, United States), 2009
- Faculty Research Award, conferred by Google - (Mountain View, California, United States), 2008
Contact
first name
- James
has last name
- Caverlee
mailing address
-
Texas A&M University
Computer Science & Engineering
3112 TAMU
College Station, TX 77843-3112
USA
Other
- caverlee@tamu.edu