Simon Dooms

590 total citations
25 papers, 426 citations indexed

About

Simon Dooms is a scholar working on Information Systems, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Simon Dooms has authored 25 papers receiving a total of 426 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Information Systems, 9 papers in Computer Networks and Communications and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Simon Dooms's work include Recommender Systems and Techniques (21 papers), Caching and Content Delivery (5 papers) and Advanced Bandit Algorithms Research (5 papers). Simon Dooms is often cited by papers focused on Recommender Systems and Techniques (21 papers), Caching and Content Delivery (5 papers) and Advanced Bandit Algorithms Research (5 papers). Simon Dooms collaborates with scholars based in Belgium and Netherlands. Simon Dooms's co-authors include Luc Martens, Toon De Pessemier, Kris Vanhecke, Babak Loni, Alan Said, Domonkos Tikk, Jan Fostier, Pieter Audenaert, Alejandro Bellogín and Sam Coppens and has published in prestigious journals such as Multimedia Tools and Applications, ACM Transactions on Intelligent Systems and Technology and Journal of Intelligent Information Systems.

In The Last Decade

Simon Dooms

25 papers receiving 408 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Simon Dooms Belgium	13	357	157	126	72	63	25	426
Ana Peleteiro Spain	10	281 0.8×	99 0.6×	109 0.9×	64 0.9×	125 2.0×	18	423
Jiahui Liu China	6	365 1.0×	234 1.5×	128 1.0×	71 1.0×	57 0.9×	18	508
Xiangwu Meng China	15	457 1.3×	229 1.5×	122 1.0×	136 1.9×	53 0.8×	67	539
Anna Maclachlan Canada	6	354 1.0×	175 1.1×	127 1.0×	70 1.0×	66 1.0×	8	478
Justin Basilico United States	9	279 0.8×	181 1.2×	111 0.9×	40 0.6×	41 0.7×	14	432
Marta Rey-López Spain	9	325 0.9×	116 0.7×	132 1.0×	82 1.1×	117 1.9×	16	445
How Jing United States	6	470 1.3×	378 2.4×	140 1.1×	55 0.8×	31 0.5×	11	606
Dominik Kowald Austria	13	214 0.6×	189 1.2×	72 0.6×	46 0.6×	54 0.9×	49	386
Tim Donkers Germany	8	301 0.8×	292 1.9×	93 0.7×	31 0.4×	48 0.8×	19	469
Ernesto Diaz-Aviles Germany	12	174 0.5×	164 1.0×	58 0.5×	49 0.7×	55 0.9×	25	367

Countries citing papers authored by Simon Dooms

Since Specialization

Citations

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

Fields of papers citing papers by Simon Dooms

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Dooms

This figure shows the co-authorship network connecting the top 25 collaborators of Simon Dooms. A scholar is included among the top collaborators of Simon Dooms 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 Simon Dooms. Simon Dooms 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

Dooms, Simon, Alejandro Bellogín, Toon De Pessemier, & Luc Martens. (2016). A Framework for Dataset Benchmarking and Its Application to a New Movie Rating Dataset. ACM Transactions on Intelligent Systems and Technology. 7(3). 1–28. 13 indexed citations

Dooms, Simon, Toon De Pessemier, & Luc Martens. (2014). Improving IMDb movie recommendations with interactive settings and filters. Ghent University Academic Bibliography (Ghent University). 1247. 3 indexed citations

Dooms, Simon, Toon De Pessemier, & Luc Martens. (2014). Online optimization for user-specific hybrid recommender systems. Multimedia Tools and Applications. 74(24). 11297–11329. 8 indexed citations

Dooms, Simon, Toon De Pessemier, & Luc Martens. (2014). Mining cross-domain rating datasets from structured data on twitter. 621–624. 7 indexed citations

Said, Alan, Simon Dooms, Babak Loni, & Domonkos Tikk. (2014). Recommender systems challenge 2014. 387–388. 21 indexed citations

Pessemier, Toon De, Simon Dooms, & Luc Martens. (2013). An Improved Data Aggregation Strategy for Group Recommendations. Ghent University Academic Bibliography (Ghent University). 1050. 36–39. 1 indexed citations

Dooms, Simon, Toon De Pessemier, & Luc Martens. (2013). MovieTweetings: a movie rating dataset collected from twitter. Ghent University Academic Bibliography (Ghent University). 78 indexed citations

Pessemier, Toon De, et al.. (2013). Social Recommendations for Events. Ghent University Academic Bibliography (Ghent University). 1066. 12 indexed citations

Pessemier, Toon De, Simon Dooms, & Luc Martens. (2013). Context-aware recommendations through context and activity recognition in a mobile environment. Multimedia Tools and Applications. 72(3). 2925–2948. 45 indexed citations

10.

Pessemier, Toon De, Simon Dooms, & Luc Martens. (2013). Comparison of group recommendation algorithms. Multimedia Tools and Applications. 72(3). 2497–2541. 88 indexed citations

11.

Pessemier, Toon De, et al.. (2013). Context-aware Recommendations through Activity Recognition. Ghent University Academic Bibliography (Ghent University). 481–490. 2 indexed citations

12.

Dooms, Simon, Toon De Pessemier, & Luc Martens. (2013). Offline optimization for user-specific hybrid recommender systems. Multimedia Tools and Applications. 74(9). 3053–3076. 10 indexed citations

13.

Dooms, Simon, Pieter Audenaert, Jan Fostier, Toon De Pessemier, & Luc Martens. (2013). In-memory, distributed content-based recommender system. Journal of Intelligent Information Systems. 42(3). 645–669. 14 indexed citations

14.

Pessemier, Toon De, Simon Dooms, & Luc Martens. (2013). A food recommender for patients in a care facility. 209–212. 12 indexed citations

15.

Dooms, Simon. (2013). Dynamic generation of personalized hybrid recommender systems. 443–446. 23 indexed citations

16.

Pessemier, Toon De, et al.. (2011). Analysis of the information value of user connections for video recommendations in a social network. Ghent University Academic Bibliography (Ghent University). 720. 1–7. 1 indexed citations

17.

Dooms, Simon, Toon De Pessemier, & Luc Martens. (2011). A user-centric evaluation of recommender algorithms for an event recommendation system. Ghent University Academic Bibliography (Ghent University). 67–73. 22 indexed citations

18.

Pessemier, Toon De, et al.. (2011). AN EVENT DISTRIBUTION PLATFORM FOR RECOMMENDING CULTURAL ACTIVITIES. Ghent University Academic Bibliography (Ghent University). 231–236. 4 indexed citations

19.

Pessemier, Toon De, Kris Vanhecke, Simon Dooms, & Luc Martens. (2011). CONTENT-BASED RECOMMENDATION ALGORITHMS ON THE HADOOP MAPREDUCE FRAMEWORK. Ghent University Academic Bibliography (Ghent University). 237–240. 14 indexed citations

20.

Pessemier, Toon De, et al.. (2010). Time dependency of data quality for collaborative filtering algorithms. Ghent University Academic Bibliography (Ghent University). 281–284. 12 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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