Pearl Huan Z. Pu
Artificial Intelligence · 2006–2021
A researcher in the Science corpus: 5 papers; trusts 31 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 68 citations into it; #10,847 of 35,345 by trust here, #15,542 by citations
- Computer Vision and Pattern Recognition — 36 citations into it; #9,817 of 21,575 by trust here, #14,151 by citations
- Natural Language Processing — 36 citations into it
- Information Systems — 34 citations into it; #716 of 8,771 by trust here, #1,102 by citations
- Management Science and Operations Research — 15 citations into it
- Sociology and Political Science — 15 citations into it; #4,359 of 22,756 by trust here, #7,052 by citations
- Information Systems and Management — 10 citations into it; #481 of 3,842 by trust here, #821 by citations
- Recommender Systems — 10 citations into it
Whom Pearl Huan Z. Pu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jonathan L. Herlocker
- J. Konstan
- Nava Tintarev
- Sean M. McNee
- John Riedl
- Fred D. Davis
- Gediminas Adomavicius
- Bo Sophia Xiao
- Judith Masthoff
- Badrul Munir Sarwar
Who trusts Pearl Huan Z. Pu overall
- Nava Tintarev
- Bart P. Knijnenburg
- Li Chen
- Judith Masthoff
- Dietmar Jannach
- Mohammed Al-Taie
- Martijn C. Willemsen
- Daniel Holliday
- Been Kim
- Simone Stumpf
Papers from Pearl Huan Z. Pu's perspective
Ordered by how strongly the researchers Pearl Huan Z. Pu trusts endorse each paper. Same corpus, a different order for every researcher.
- Explaining collaborative filtering recommendations
- Evaluating collaborative filtering recommender systems
- Item-based collaborative filtering recommendation algorithms
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Is seeing believing?: how recommender system interfaces affect users' opinions (International Conference on Human Factors in Computing Systems)
- Analysis of recommendation algorithms for e-commerce (ACM Conference on Economics and Computation)
- Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
- Getting to know you: learning new user preferences in recommender systems
- Improving recommendation lists through topic diversification
- Fab: content-based, collaborative recommendation
Their papers
- Natural Language Processing with Commonsense Knowledge: A Survey (2021) — 8 citations
- Evaluating recommender systems from the user’s perspective: survey of the state of the art (2012) — 400 citations
- A user-centric evaluation framework for recommender systems (2011) — 936 citations
- Critiquing-based recommenders: survey and emerging trends (2012) — 313 citations
- Trust building with explanation interfaces (2006) — 336 citations