Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Rank and relevance in novelty and diversity metrics for recommender systems
2011429 citationsSaúl Vargas, Pablo CastellsTesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Saúl Vargas's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Saúl Vargas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saúl Vargas more than expected).
This network shows the impact of papers produced by Saúl Vargas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Saúl Vargas. The network helps show where Saúl Vargas may publish in the future.
Co-authorship network of co-authors of Saúl Vargas
This figure shows the co-authorship network connecting the top 25 collaborators of Saúl Vargas.
A scholar is included among the top collaborators of Saúl Vargas based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Saúl Vargas. Saúl Vargas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Vargas, Saúl, et al.. (2019). Learning Embeddings for Product Size Recommendations.. International ACM SIGIR Conference on Research and Development in Information Retrieval.4 indexed citations
Vargas, Saúl, et al.. (2016). Mendeley. 365–365.8 indexed citations
8.
McCreadie, Richard, et al.. (2015). University of Glasgow at TREC 2015: Experiments with Terrier in Contextual Suggestion, Temporal Summarisation and Dynamic Domain Tracks. Text REtrieval Conference.3 indexed citations
Vargas, Saúl, et al.. (2012). On the suitability of intent spaces for IR diversification. Biblos-e Archivo (Universidad Autónoma de Madrid).1 indexed citations
Castells, Pablo, Saúl Vargas, & Jun Wang. (2011). Novelty and diversity metrics for recommender systems: Choice, discovery and relevance. Biblos-e Archivo (Universidad Autónoma de Madrid).74 indexed citations
18.
Vargas, Saúl & Pablo Castells. (2011). Rank and relevance in novelty and diversity metrics for recommender systems. Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya). 109–116.429 indexed citations breakdown →
19.
Mendes, Ricardo, et al.. (1998). A Web Based Environment For Construction Planning Courses 1. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas).1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.