Max Chevalier

813 citations
36 papers · 214 · h-index 9

Impact in

Papers in

    • Recommender Systems and Techniques 10
    • Information Retrieval and Search Behavior 5
    • Web Data Mining and Analysis 5
    • Semantic Web and Ontologies 11
    • Advanced Text Analysis Techniques 4

Max Chevalier

32 papers receiving 201 citations

Peers

Max Chevalier
Comparison fields: 5 of 56
  • Signal Processing 45
  • Information Systems 87
  • Computer Networks and Communications 70
  • Artificial Intelligence 83
  • Management Information Systems 20
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Citations per field
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Citations per year

Countries citing papers authored by Max Chevalier

Since Specialization
Citations

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

Fields of papers citing papers by Max Chevalier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Max Chevalier, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Max Chevalier Line = papers co-authored together Max Chevalier links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202236
2 201529
3 201521
4 200717
5
A Social Validation of Collaborative Annotations on Digital Documents
200512
6 200812
7 20118
8 20078
9 20148
10 20076
11 20115
12
Diversity in Recommender Systems: Bridging the gap between users and systems
20115
13
Querying heterogeneous data in graph-oriented NoSQL systems
20184
14 20204
15
ISIDOR: A Visualisation Interface for Advanced Information Retrieval.
20003
16 20113
17 20103
18 20093
19 20203
20 20223

About Max Chevalier

Max Chevalier is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics and Information Systems and Management, having authored 36 papers that have together received 214 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (11 papers), Recommender Systems and Techniques (10 papers), Advanced Database Systems and Queries (5 papers), Information Retrieval and Search Behavior (5 papers), Web Data Mining and Analysis (5 papers), Data Management and Algorithms (4 papers), Advanced Text Analysis Techniques (4 papers) and Personal Information Management and User Behavior (4 papers). The work is most often cited by research in Signal Processing (45 citations), Information Systems (87 citations), Computer Networks and Communications (70 citations), Artificial Intelligence (83 citations) and Management Information Systems (20 citations). Max Chevalier has collaborated with scholars based in France, Italy and Germany. Frequent co-authors include Olivier Teste, Christine Julien, Guillaume Cabanac, Ronan Tournier, Arlind Kopliku, Claude Chrisment, Corentin Lapeyre, Franck Ravat, Valentin Grouès and André Tricot. Their work appears in journals such as Information Retrieval, ACM SIGIR Forum, Computational Geosciences, International Journal on Digital Libraries and Aerospace Science and Technology.

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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