Tribler Learning-to-Rank Dataset

DOI:10.4121/17091504-6005-4ae0-8a30-468675f09d3c.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/17091504-6005-4ae0-8a30-468675f09d3c
Datacite citation style:
Gregoriadis, Marcel; Stokkink, Quinten (2025): Tribler Learning-to-Rank Dataset. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/17091504-6005-4ae0-8a30-468675f09d3c.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

The Tribler Learning-to-Rank (LTR) Dataset is a collection of feature vectors derived from a decentralized search engine conducted within the Tribler peer-to-peer file-sharing network. This dataset was created to train and evaluate DART, an LTR-based algorithm for relevance ranking in decentralized file-sharing systems.

The dataset supports research into decentralized search and ranking techniques by providing feature vectors extracted from real user interactions. It allows researchers to develop, validate, and compare LTR models for the decentralized setting.


This dataset comprises rows containing a binary relevance judgment, a query ID, and 31 normalized features.


Example:

```

1 qid:8030 0:0.311253027575435 1:0.3623667000954467 2:0.2640038215062309 3:0.2908247887862055 4:0.0583718121596237 5:-0.18465409343274 6:1.327852667807301 7:1.315861763360352 8:1.509079253500692 9:-0.1600325676141462 10:-0.5992397181984241 11:0.5291165364861627 12:0.4186126253353217 13:0.4601304198871708 14:0.2818017229171959 15:-0.237928002400229 16:0.1731179222782891 17:0.0738413823487902 18:0.3099197449886553 19:0.1805433703801239 20:0.1158592011796661 21:-0.665142871178777 22:0.3328536662895701 23:0.2948988525040041 24:0.7315729714005331 25:-0.6902133277846004 26:2.62527037747493 27:-0.3083199080468793 28:0.5082540870914015 29:-1.084397975003081 30:-5.408917243099888


0 qid:8030 0:0.2625962288699837 1:0.3623667000954467 2:0.2640038215062309 3:0.2908247887862055 4:0.0583718121596237 5:-0.18465409343274 6:1.327852667807301 7:1.315861763360352 8:1.509079253500692 9:-0.1600325676141462 10:-0.5992397181984241 11:0.5291165364861627 12:0.4186126253353217 13:0.4601304198871708 14:0.2818017229171959 15:-0.237928002400229 16:0.1994409565033298 17:0.0738413823487902 18:0.3099197449886553 19:0.02724546833125713 20:0.4530615272328508 21:-0.665142871178777 22:0.3328536662895701 23:0.2948988525040041 24:0.6189088582085388 25:-0.6902133277846004 26:-0.3433711973076669 27:-0.3083199080468793 28:0.5082540870914015 29:-1.637896085379517 30:-2.427152848520973

```


Please cite the following article when using this dataset:

**to be added**

History

  • 2025-02-17 first online, published, posted

Publisher

4TU.ResearchData

Format

{train,vali,test}.txt (gzipped)

Funding

  • Dutch national NWO/TKI science grant BLOCK.2019.004 (grant code BLOCK.2019.004) NWO/TKI

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology, Data-intensive Systems

DATA

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