Shyong K. Lam
Information Systems · 2002–2011
A researcher in the Science corpus: 7 papers; trusts 11 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 26 citations into it; #877 of 8,771 by trust here, #364 by citations
- Artificial Intelligence — 11 citations into it; #26,420 of 35,345 by trust here, #11,750 by citations
- Recommender Systems — 9 citations into it
- Management Science and Operations Research — 7 citations into it; #2,265 of 11,946 by trust here, #844 by citations
- Sociology and Political Science — 6 citations into it
- Computer Science Applications — 5 citations into it; #1,381 of 1,855 by trust here, #617 by citations
Whom Shyong K. Lam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Paul Resnick
- Badrul Munir Sarwar
- John F. Canny
- Chrysanthos Dellarocas
- Michael P. O'Mahony
- J. Konstan
- J. Breese
- Jonathan L. Herlocker
- George Karypis
- John Riedl
Who trusts Shyong K. Lam overall
- Bamshad Mobasher
- J. Konstan
- Gediminas Adomavicius
- John Riedl
- J. Schafer
- Nava Tintarev
- Jonathan L. Herlocker
- Mehrbakhsh Nilashi
- Michael D. Ekstrand
- Robin D. Burke
Papers from Shyong K. Lam's perspective
Ordered by how strongly the researchers Shyong K. Lam trusts endorse each paper. Same corpus, a different order for every researcher.
- Using collaborative filtering to weave an information tapestry
- An algorithmic framework for performing collaborative filtering
- Analysis of recommendation algorithms for e-commerce (ACM Conference on Economics and Computation)
- An open architecture for collaborative filtering of netnews
- Application of Dimensionality Reduction in Recommender System - A Case Study
- Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior (ACM Conference on Economics and Computation)
- Empirical Analysis of Predictive Algorithms for Collaborative Filtering
- Fab: content-based, collaborative recommendation
- Eigentaste: A Constant Time Collaborative Filtering Algorithm
- Getting to know you: learning new user preferences in recommender systems
Their papers
- Getting to know you: learning new user preferences in recommender systems (2002) — 677 citations
- Is seeing believing?: how recommender system interfaces affect users' opinions (2003) — 561 citations
- Shilling recommender systems for fun and profit (2004) — 755 citations
- MovieLens unplugged: experiences with an occasionally connected recommender system (2003) — 613 citations
- WP:clubhouse?: an exploration of Wikipedia's gender imbalance (2011) — 232 citations
- Creating, destroying, and restoring value in wikipedia (2007) — 384 citations
- On the recommending of citations for research papers (2002) — 455 citations