Daniel Valcarce

449 total citations
20 papers, 247 citations indexed

About

Daniel Valcarce is a scholar working on Information Systems, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Daniel Valcarce has authored 20 papers receiving a total of 247 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Information Systems, 8 papers in Artificial Intelligence and 5 papers in Management Science and Operations Research. Recurrent topics in Daniel Valcarce's work include Recommender Systems and Techniques (17 papers), Information Retrieval and Search Behavior (7 papers) and Topic Modeling (7 papers). Daniel Valcarce is often cited by papers focused on Recommender Systems and Techniques (17 papers), Information Retrieval and Search Behavior (7 papers) and Topic Modeling (7 papers). Daniel Valcarce collaborates with scholars based in Spain, Brazil and Italy. Daniel Valcarce's co-authors include Javier Parapar, Álvaro Barreiro, Alejandro Bellogín, Pablo Castells, Ángel León, Jesús F. San Miguel, Enric Carreras, Jorge Sierra, Josè Antonio Pérez-Simón and Álvaro Urbano and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and Knowledge-Based Systems.

In The Last Decade

Daniel Valcarce

20 papers receiving 239 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Valcarce Spain	9	194	119	57	51	22	20	247
Aghiles Salah Singapore	10	210 1.1×	181 1.5×	42 0.7×	107 2.1×	19 0.9×	14	305
Flavian Vasile United States	8	198 1.0×	192 1.6×	83 1.5×	57 1.1×	16 0.7×	20	290
Jiankai Sun United States	9	175 0.9×	149 1.3×	46 0.8×	33 0.6×	11 0.5×	12	250
João Vinagre Portugal	11	179 0.9×	139 1.2×	67 1.2×	57 1.1×	15 0.7×	24	261
Leo Iaquinta Italy	7	151 0.8×	89 0.7×	33 0.6×	58 1.1×	10 0.5×	17	230
Tianxin Wei United States	5	247 1.3×	218 1.8×	107 1.9×	42 0.8×	14 0.6×	8	307
Benjamin Kille Germany	8	209 1.1×	123 1.0×	63 1.1×	89 1.7×	6 0.3×	32	255
Victor E. Lee United States	6	106 0.5×	98 0.8×	19 0.3×	46 0.9×	15 0.7×	8	188
Andreas Lommatzsch Germany	8	147 0.8×	106 0.9×	33 0.6×	51 1.0×	7 0.3×	30	197

Countries citing papers authored by Daniel Valcarce

Since Specialization

Citations

This map shows the geographic impact of Daniel Valcarce'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 Daniel Valcarce with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Valcarce more than expected).

Fields of papers citing papers by Daniel Valcarce

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Valcarce. 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 Daniel Valcarce. The network helps show where Daniel Valcarce may publish in the future.

Co-authorship network of co-authors of Daniel Valcarce

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Valcarce. A scholar is included among the top collaborators of Daniel Valcarce 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 Daniel Valcarce. Daniel Valcarce is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Valcarce, Daniel, et al.. (2020). The Coruña corpus tool: Ten years on. Procesamiento del lenguaje natural. 64(64). 13–19. 1 indexed citations

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2020). A Distributed Recommendation Platform for Big Data. JUCS - Journal of Universal Computer Science. 21. 1810–1829. 2 indexed citations

Valcarce, Daniel, Alejandro Bellogín, Javier Parapar, & Pablo Castells. (2020). Assessing ranking metrics in top-N recommendation. Information Retrieval. 23(4). 411–448. 38 indexed citations

Valcarce, Daniel, et al.. (2019). Collaborative filtering embeddings for memory-based recommender systems. Engineering Applications of Artificial Intelligence. 85. 347–356. 42 indexed citations

Valcarce, Daniel. (2019). Information retrieval models for recommender systems. ACM SIGIR Forum. 53(1). 44–45. 1 indexed citations

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2019). Document-based and term-based linear methods for pseudo-relevance feedback. ACM SIGAPP Applied Computing Review. 18(4). 5–17. 6 indexed citations

Valcarce, Daniel, et al.. (2018). Term Association Measures for Memory-based Recommender Systems. 1–8. 1 indexed citations

Valcarce, Daniel, et al.. (2018). When Diversity Met Accuracy: A Story of Recommender Systems. SHILAP Revista de lepidopterología. 1178–1178. 1 indexed citations

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2018). A MapReduce implementation of posterior probability clustering and relevance models for recommendation. Engineering Applications of Artificial Intelligence. 75. 114–124. 4 indexed citations

10.

Valcarce, Daniel, Alejandro Bellogín, Javier Parapar, & Pablo Castells. (2018). On the robustness and discriminative power of information retrieval metrics for top-N recommendation. 260–268. 47 indexed citations

11.

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2018). Finding and analysing good neighbourhoods to improve collaborative filtering. Knowledge-Based Systems. 159. 193–202. 17 indexed citations

12.

Valcarce, Daniel, et al.. (2018). Item-driven group formation. ISTI Open Portal. 8. 17–31. 1 indexed citations

13.

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2018). LiMe. 678–687. 6 indexed citations

14.

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2017). Axiomatic Analysis of Language Modelling of Recommender Systems. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 25(Suppl. 2). 113–127. 5 indexed citations

15.

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2017). Combining Top-N Recommenders with Metasearch Algorithms. 805–808. 8 indexed citations

16.

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2016). Item-based relevance modelling of recommendations for getting rid of long tail products. Knowledge-Based Systems. 103. 41–51. 29 indexed citations

17.

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2016). Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems. 1–8. 13 indexed citations

18.

Valcarce, Daniel. (2015). Exploring Statistical Language Models for Recommender Systems. 375–378. 9 indexed citations

19.

Valcarce, Daniel, Javier Parapar, & Álvaro Barreiro. (2015). A Study of Priors for Relevance-Based Language Modelling of Recommender Systems. 237–240. 6 indexed citations

20.

Díez‐Campelo, María, Josè Antonio Pérez-Simón, Rodrigo Martino, et al.. (2004). Influence of the Intensity of the Conditioning Regimen on the Characteristics of Acute and Chronic Graft Versus Host Disease after Allogeneic Transplantation.. Blood. 104(11). 5157–5157. 10 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.

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