OpeNTF: An Open-Source Neural Team Formation Benchmark Library

Team formation involves selecting a team of skillful experts who will, more likely than not, accomplish a task. Researchers have proposed a rich body of computational methods to automate the traditionally tedious and error-prone manual process. We previously released OpeNTF, an open-source framework hosting canonical neural models as the cutting-edge class of approaches, along with large-scale training datasets from varying domains. In this paper, we contribute OpeNTF2 that extends the initial release in two prime directions. (1) The first of its kind in neural team formation, we integrated debiasing reranking algorithms to mitigate the popularity and gender disparities in the neural models’ team recommendations based on two alternative notions of fairness: equality of opportunity and demographic parity. (2) We further contribute a temporal training strategy for neural models’ training to capture the evolution of experts’ skills and collaboration ties over time, as opposed to randomly shuffled training datasets. OpeNTF2 is a forward-looking effort to automate team formation via fairness-aware and time-sensitive methods. AI-ML-based solutions are increasingly impacting how resources are allocated to various groups in society, and ensuring fairness and time are systematically considered is key.

1. Setup 2. Quickstart 3. Features Fairness aware Team Formation Datasets and Parallel Preprocessing Non-Temporal Neural Team Formation Temporal Neural Team Prediction Model Architecture Negative Sampling Strategies Run 4. Results 5. Acknowledgement 6. License 7. Citation 8. Awards

1. Setup

You need to have Python >= 3.8 and install the following main packages, among others listed in requirements.txt:

torch>=1.9.0
pytrec-eval-terrier==0.5.2
gensim==3.8.3

By pip, clone the codebase and install required packages:

git clone --recursive https://github.com/Fani-Lab/opentf
cd opentf
pip install -r requirements.txt

By conda:

git clone --recursive https://github.com/Fani-Lab/opentf
cd opentf
conda env create -f environment.yml
conda activate opentf

For installation of specific version of a python package due to, e.g., CUDA versions compatibility, one can edit requirements.txt or environment.yml like as follows:

# CUDA 10.1
torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html

2. Quickstart

cd src
python -u main.py -data ../data/raw/dblp/toy.dblp.v12.json -domain dblp -model fnn bnn -fairness det_greedy -attribute popularity

The above run, loads and preprocesses a tiny-size toy example dataset toy.dblp.v12.json from dblp followed by n-fold train-evaluation on a training split and final test on the test set for feedforward and Bayesian neural models using default hyperparameters from ./src/param.py. Then, the predictions will be passed through the det_greedy reranking fairness algorithm to mitigate popularity bias in teams with default k_max, np_ratio fromn ./src/param.py.

python -u main.py -data ../data/raw/dblp/toy.dblp.v12.json -domain dblp -model tbnn tbnn_dt2v_emb

This script loads and preprocesses the same dataset toy.dblp.v12.json from dblp, takes the teams from the the last year as the test set and trains the Bayesian neural model following our proposed streaming training strategy as explained in 3.2.2. Temporal Neural Team Formation with two different input representations i) sparse vector represntation and ii) temporal skill vector represntation using default hyperparameters from ./src/param.py.

3. Features

3.1. Adila: Fairness aware Team Formation

While state-of-the-art neural team formation methods are able to efficiently analyze massive collections of experts to form effective collaborative teams, they largely ignore the fairness in recommended teams of experts. In Adila, we study the application of fairness-aware team formation algorithms to mitigate the potential popularity bias in the neural team formation models. We support two fairness notions namely, equality of opportunity and demographic parity. To achieve fairness, we utilize three deterministic greedy reranking algorithms (det_greedy, det_cons, det_relaxed) in addition to fa*ir, a probabilistic greedy reranking algorithm .

For further details and demo, please visit Adila's submodule.

3.2. Datasets and Parallel Preprocessing

Raw dataset, e.g., scholarly papers from AMiner's citation network dataset of dblp, movies from imdb, or US patents from uspt were assumed to be populated in data/raw. For the sake of integration test, tiny-size toy example datasets toy.dblp.v12.json from dblp, [toy.title.basics.tsv, toy.title.principals.tsv, toy.name.basics.tsv] from imdb and toy.patent.tsv have been already provided.

Raw data will be preprocessed into two main sparse matrices each row of which represents:

i) vecs['member']: occurrence (boolean) vector representation for members of a team, e.g., authors of a paper or crew members of a movie,

ii) vecs['skill']: occurrence (boolean) vector representation for required skills for a team, e.g., keywords of a paper or genre of a movie.

Also, indexes will be created to map the vector's indexes to members' names and skills' names, i.e., i2c, c2i, i2s, s2i.

The sparse matrices and the indices will be persisted in data/preprocessed/{dblp,imdb,uspt}/{name of dataset} as pickles teamsvecs.pkl and indexes.pkl. For example, the preprocessed data for our dblp toy example are data/preprocessed/dblp/toy.dblp.v12.json/teamsvecs.pkl and data/preprocessed/dblp/toy.dblp.v12.json/indexes.pkl.

Our pipeline benefits from parallel generation of sparse matrices for teams that significantly reduces the preprocessing time as shown below:

Please note that the preprocessing step will be executed once. Subsequent runs load the persisted pickle files. In order to regenerate them, one should simply delete them.

3.3. Non-Temporal Neural Team Formation

We randomly take 85% of the dataset for the train-validation set and 15% as the test set, i.e., the model never sees these instances during training or model tuning. You can change train_test_split parameter in ./src/param.py.

3.4. Temporal Neural Team Prediction

Previous works in team formation presumed that teams follow the i.i.d property and hence when training their models they followed the bag of teams approach, where they train and validate their models on a shuffled dataset of teams. Moreover, they were interpolative and did not try to predict future successful teams. In this work, we aim at extrapolating and predicting future teams of experts. We sort the teams by time intervals and train a neural model incrementally through the ordered collection of teams in [C₀, ..C_t, ..C_T]. As can be seen in Figure below, after random initialization of skills’ and experts’ embeddings at t=0, we start training the model on the teams in the first time interval C₀ for a number of epochs, then we continue with training on the second time interval C₁ using the learned embeddings from the previous time interval and so forth until we finish the training on the last training time interval C_t=T. We believe that using this approach, will help the model understand how experts’ skills and collaborative ties evolve through time and the final embeddings are their optimum representation in the latent space to predict future successful teams at time interval C_t=T+1.

3.5. Model Architecture

Each model has been defined in ./src/mdl/ under an inheritance hierarchy. They override abstract functions for train, test, eval, and plot steps.

For example, for our feedforward baseline fnn, the model has been implemented in ./src/mdl/fnn.py. Model's hyperparameters such as the learning rate (lr) or the number of epochs (e) can be set in ./src/param.py.

Currently, we support neural models:

1. Bayesian bnn where model's parameter (weights) is assumed to be drawn from Gaussian (Normal) distribution and the task is to not to learn the weight but the mean (μ) and standard deviation (σ) of the distribution at each parameter.

2. non-Bayesian feedforward fnn where the model's parameter (weights) is to be learnt.

The input to the models is the vector representations for (temporal) skills and the output is the vector representation for members. In another word, given the input skills, the models predict the members from the pool of candidates. We support three vector representations:

i) Sparse vector representation (occurrence or boolean vector): See preprocessing section above.

ii) Dense vector representation (team2vec): Inspired by paragraph vectors by Le and Mikolov, we consider a team as a document and skills as the document words (embtype == 'skill'). Using distributed memory model, we map skills into a real-valued embedding space. Likewise and separately, we consider members as the document words and map members into real-valued vectors (embtype == 'member'). We also consider mapping skills and members into the same embedding space (embtype == 'joint'). Our embedding method benefits from gensim library.

iii) Temporal skill vector represntation (team2vec): Inspired by Hamilton et al., we also incorporate time information into the underlying neural model besides utilizing our proposed streaming training strategy. We used the distributed memory model of Doc2Vec to generate the real-valued joint embeddings of the subset of skills and time intervals, where the skills and time intervals are the words of the document (embtype == 'dt2v').

3. In OpeNTF2, The Nmt wrapper class is designed to make use of advanced transformer models and encoder-decoder models that include multiple LSTM or GRU cells, as well as various attention mechanisms. Nmt is responsible for preparing the necessary input and output elements and invokes the executables of opennmt-py by creating a new process using Python's subprocess module. Additionally, because the Nmt wrapper class inherits from Ntf, these models can also take advantage of temporal training strategies through tNtf.

3.6. Negative Sampling Strategies

As known, employing unsuccessful teams convey complementary negative signals to the model to alleviate the long-tail problem. Most real-world training datasets in the team formation domain, however, do not have explicit unsuccessful teams (e.g., collections of rejected papers.) In the absence of unsuccessful training instances, we proposed negative sampling strategies based on the closed-world assumption where no currently known successful group of experts for the required skills is assumed to be unsuccessful. We study the effect of three different negative sampling strategies: two based on static distributions, and one based on adaptive noise distribution:

1. Uniform distribution (uniform), where subsets of experts are randomly chosen with the same probability as unsuccessful teams from the uniform distribution over all subsets of experts.

2. Unigram distribution (unigram), where subsets of experts are chosen regarding their frequency in all previous successful teams. Intuitively, teams of experts that have been more successful but for other skill subsets will be given a higher probability and chosen more frequently as a negative sample to dampen the effect of popularity bias.

3. Smoothed unigram distribution in each training minibatch (unigram_b), where we employed the add-1 or Laplace smoothing when computing the unigram distribution of the experts but in each training minibatch. Minibatch stochastic gradient descent is the de facto method for neural models where the data is split into batches of data, each of which is sent to the model for the partial calculation to speed up training while maintaining high accuracy.

To include a negative sampling strategy, there are two parameters for a model to set in ./src/param.py:

• ns: the negative sampling strategy which can be uniform, unigram, unigram_b or None(no negative sampling).
• nns: number of negative samples

3.7. Run

The pipeline accepts three required list of values:

1. -data: list of path to the raw datafiles, e.g., -data ./../data/raw/dblp/dblp.v12.json, or the main file of a dataset, e.g., -data ./../data/raw/imdb/title.basics.tsv
2. -domain: list of domains of the raw data files that could be dblp, imdb, or uspt; e.g., -domain dblp imdb.
3. -model: list of baseline models that could be fnn, fnn_emb, bnn, bnn_emb, tfnn, tfnn_emb, tfnn_dt2v_emb, tbnn, tbnn_emb, tbnn_dt2v_emb, random; e.g., -model random fnn bnn tfnn tbnn tfnn_dt2v_emb tbnn_dt2v_emb.

Here is a brief explanation of the models:

• fnn, bnn, fnn_emb, bnn_emb: follows the standard machine learning training procedure.
• tfnn, tbnn, tfnn_emb, tbnn_emb: follows our proposed streaming training strategy without adding temporal information to the input of the models.
• tfnn_dt2v_emb, tbnn_dt2v_emb: follows our proposed streaming training strategy and employs temporal skills as input of the models.

4. Results

We used pytrec_eval to evaluate the performance of models on the test set as well as on their own train sets (should overfit) and validation sets. We report the predictions, evaluation metrics on each test instance, and average on all test instances in ./output/{dataset name}/{model name}/{model's running setting}/. For example:

1. f0.test.pred is the predictions per test instance for a model which is trained folds [1,2,3,4] and validated on fold [0].
2. f0.test.pred.eval.csv is the values of evaluation metrics for the predictions per test instance
3. f0.test.pred.eval.mean.csv is the average of values for evaluation metrics over all test instances.
4. test.pred.eval.mean.csv is the average of values for evaluation metrics over all n fold models.

Benchmarks at Scale

1. Fair Team Formation Results

	min. #member's team: 75, min team size: 3, epochs: 20, learning rate: 0.1, hidden layer: [1, 100d], minibatch: 4096, #negative samples: 3
Datasets	dblp.v12, imdb, uspt (running ...)
Metrics	ndkl, map@2,5,10, ndcg@2,5,10, auc
Sensitive Attributes	popularity, gender(running ...)
Baselines	{bnn, random}×{sparse, emb}×{unigram_b}
Results	for further details and results, please visit Adila's submodule

Average performance of 5-fold neural models on the test set of the imdb, dblp and uspt datasets. For the metrics: ndkl, lower values are better (↓); skew, values closer to 0 are better (→0); and map and ndcg, higher values are better (↑).

---

imdb dataset

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dblp table

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uspt table

2. Non-Temporal Neural Team Formation

	min. #member's team: 75, min team size: 3, epochs: 20, learning rate: 0.1, hidden layer: [1, 100d], minibatch: 4096, #negative samples: 3
Datasets	dblp.v12, imdb, uspt
Metrics	recall@2,5,10, map@2,5,10, ndcg@2,5,10, p@2,5,10, auc
Baselines	{fnn,bnn}×{sparse, emb}×{none, uniform, unigram, unigram_b}
Results	./output/dblp.v12.json.filtered.mt75.ts3/, ./output/title.basics.tsv.filtered.mt75.ts3/

Full predictions of all models on test and training sets and the values of evaluation metrics, per instance and average, are available in a rar file of size 74.8GB and will be delivered upon request!

3. Temporal Neural Team Prediction

We kick-started our experiments based on the best results from the non-temporal neural team formation experiments.

	min. #member's team: 75, min team size: 3, epochs: 20, learning rate: 0.1, hidden layer: [1, 128d], minibatch: 128, #negative samples: 3
Datasets	dblp.v12, imdb, uspt, gith
Metrics	recall@2,5,10, map@2,5,10, ndcg@2,5,10, p@2,5,10, auc
Baselines	{bnn, tbnn}×{sparse, emb, dt2v_emb}×{unigram_b},{rrn}
Results	./output/dblp.v12.json.filtered.mt75.ts3/, ./output/title.basics.tsv.filtered.mt75.ts3/, ./output/patent.tsv.filtered.mt75.ts3/

Full predictions of all models on test and training sets and the values of evaluation metrics are available in a rar file and will be delivered upon request!

5. Acknowledgement:

We benefit from pytrec_eval, gensim, Josh Feldman's blog, and other libraries. We would like to thank the authors of these libraries and helpful resources.

6. License:

©2021. This work is licensed under a CC BY-NC-SA 4.0 license.

Arman Dashti¹, Hossein Fani^1,2

^{1School of Computer Science, Faculty of Science, University of Windsor, ON, Canada. 2[email protected]}

7. Citation:

@inproceedings{DBLP:conf/ecir/FaniBDS24,
  author    = {Hossein Fani and Reza Barzegar and Arman Dashti and Mahdis Saeedi},
  title     = {A Training Strategy for Future Collaborative Team Prediction},
  booktitle = {Advances in Information Retrieval - 46th European Conference on Information Retrieval, {ECIR} 2024, Glasgow, Scotland, March 24th-28, 2024},
  series    = {Lecture Notes in Computer Science},
  volume    = {},
  pages     = {},
  publisher = {Springer},
  year      = {2024},
  url       = {https://doi.org/},
  doi       = {},
  biburl    = {},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/cikm/DashtiSF22,
  author    = {Arman Dashti and Saeed Samet and Hossein Fani},
  title     = {Effective Neural Team Formation via Negative Samples},
  booktitle = {Proceedings of the 31st {ACM} International Conference on Information {\&} Knowledge Management, Atlanta, GA, USA, October 17-21, 2022},
  pages     = {3908--3912},
  publisher = {{ACM}},
  year      = {2022},
  url       = {https://doi.org/10.1145/3511808.3557590},
  doi       = {10.1145/3511808.3557590},
  biburl    = {https://dblp.org/rec/conf/cikm/DashtiSF22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/cikm/DashtiSPF22,
author    = {Arman Dashti and Karan Saxena and Dhwani Patel and Hossein Fani},
title     = {OpeNTF: {A} Benchmark Library for Neural Team Formation},
booktitle = {Proceedings of the 31st {ACM} International Conference on Information {\&} Knowledge Management, Atlanta, GA, USA, October 17-21, 2022},
pages     = {3913--3917},
publisher = {{ACM}},
year      = {2022},
url       = {https://doi.org/10.1145/3511808.3557526},
doi       = {10.1145/3511808.3557526},
biburl    = {https://dblp.org/rec/conf/cikm/DashtiSPF22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

8. Awards:

CAD$300, Gold medalist, UWill Discover, 2022

CAD$300, Best Research, Demo Day, School of Computer Science, University of Windsor, 2022.