Saul Vargas

Information Systems · 2011–2015

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 33 citations into it; #3,651 of 8,771 by trust here, #4,012 by citations
• Artificial Intelligence — 18 citations into it
• Management Science and Operations Research — 18 citations into it; #5,339 of 11,946 by trust here, #4,746 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Signal Processing — 8 citations into it
• Information Retrieval & Search — 5 citations into it
• Marketing — 5 citations into it; #2,391 of 6,289 by trust here, #3,201 by citations

Whom Saul Vargas trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alistair Moffat
2. Filip Radlinski
3. Olivier Chapelle
4. J. Breese
5. Justin Zobel
6. Robert D. Kleinberg
7. Yehuda Koren
8. Sean M. McNee
9. David E. Heckerman
10. Donald Metlzer

Who trusts Saul Vargas overall

1. Marius Kaminskas
2. Himanshu Jain
3. Derek G. Bridge
4. Yashoteja Prabhu
5. Bart P. Knijnenburg
6. Amin Javari
7. Manik Varma
8. Jessica Su
9. Natali Helberger

Papers from Saul Vargas's perspective

Ordered by how strongly the researchers Saul Vargas trusts endorse each paper. Same corpus, a different order for every researcher.

1. Evaluating collaborative filtering recommender systems
2. Rank-biased precision for measurement of retrieval effectiveness
3. Empirical Analysis of Predictive Algorithms for Collaborative Filtering
4. Learning diverse rankings with multi-armed bandits
5. Novelty and diversity in information retrieval evaluation (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval)
6. Matrix Factorization Techniques for Recommender Systems
7. Expected reciprocal rank for graded relevance
8. Being accurate is not enough: how accuracy metrics have hurt recommender systems (CHI Extended Abstracts)
9. Cumulated gain-based evaluation of IR techniques
10. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries

Their papers

• Rank and relevance in novelty and diversity metrics for recommender systems (2011) — 755 citations
• Novelty and Diversity in Recommender Systems (2015) — 399 citations