Vol-3194/paper60
Wolfgang Fahl
Paper
| Paper | |
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 edit
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| description | |
| id | Vol-3194/paper60 |
| wikidataid | Q117344891→Q117344891 |
| title | The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper60.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/AnelliBFMMPDSN22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Paper[edit]
| Paper | |
|---|---|
| edit
| |
| description | |
| id | Vol-3194/paper60 |
| wikidataid | Q117344891→Q117344891 |
| title | The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper60.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/AnelliBFMMPDSN22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models[edit]
The Challenging Reproducibility
Task in Recommender Systems Research
between Traditional and Deep Learning Models
Discussion Paper
Vito Walter Anelli1 ,
Alejandro Bellogín2 , Antonio Ferrara1 , Daniele Malitesta1 , Felice Antonio Merra4 ,
Claudio Pomo1 , Francesco Maria Donini3 , Eugenio Di Sciascio1 and Tommaso Di Noia1
1
Politecnico di Bari, via Orabona, 4, 70125 Bari, Italy
2
Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
3
Università degli Studi della Tuscia, via Santa Maria in Gradi, 4, 01100 Viterbo, Italy
4
Amazon Science Berlin, Invalidenstraße 75, 10557 Berlin, Germany
Abstract
Recommender Systems have shown to be a useful tool for reducing over-choice and providing accurate,
personalized suggestions. The large variety of available recommendation algorithms, splitting techniques,
assessment protocols, metrics, and tasks, on the other hand, has made thorough experimental evaluation
extremely difficult. Elliot is a comprehensive framework for recommendation with the goal of running and
reproducing a whole experimental pipeline from a single configuration file. The framework uses a variety
of ways to load, filter, and divide data. Elliot optimizes hyper-parameters for a variety of recommendation
algorithms, then chooses the best models, compares them to baselines, computes metrics ranging from
accuracy to beyond-accuracy, bias, and fairness, and does statistical analysis. The aim is to provide
researchers with a tool to ease all the experimental evaluation phases (and make them reproducible), from
data reading to results collection. Elliot is freely available on GitHub at https://github.com/sisinflab/elliot.
Keywords
Recommender Systems, Reproducibility, Adversarial Learning, Visual Recommenders, Knowledge Graphs
1. Introduction
In the last decade, Recommendation Systems (RSs) have gained momentum as the pivotal choice
for personalized decision-support systems. Recommendation is essentially a retrieval task where
a catalog of items is ranked in a personalized way and the top-scoring items are presented to the
user [1]. Once the RSs ability to provide personalized items to clients had been demonstrated,
both academia and industry began to devote attention to them [2]. This collective effort resulted
in an impressive number of recommendation algorithms, ranging from memory-based [3] to
latent factor-based [4, 5], as well as deep learning-based methods [6]. At the same time, the RS
research community realized that focusing only on the accuracy of results could be detrimental,
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ vitowalter.anelli@poliba.it (V. W. Anelli); claudio.pomo@poliba.it (C. Pomo)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
�and started exploring beyond-accuracy evaluation [7]. As accuracy was recognized as insufficient
to guarantee users’ satisfaction [8], novelty and diversity [9, 10] came into play as new dimensions
to be analyzed when comparing algorithms. However, this was only the first step in the direction
of a more comprehensive evaluation. Indeed, more recently, the presence of biased [11] and unfair
recommendations towards user groups and item categories [12] has been widely investigated.
The abundance of possible choices has generated confusion around choosing the correct base-
lines, conducting the hyperparameter optimization and the experimental evaluation [13], and
reporting the details of the adopted procedure. Consequently, two major concerns have arisen:
unreproducible evaluation and unfair comparisons [14].
The advent of various frameworks over the last decade has improved the research process, and
the RS community has gradually embraced the emergence of recommendation, assessment, and
even hyperparameter tweaking frameworks. Starting from 2011, Mymedialite [15], LensKit [16],
LightFM [17], RankSys [9], and Surprise [18], have formed the basic software for rapid prototyp-
ing and testing of recommendation models, thanks to an easy-to-use model execution and the
implementation of standard accuracy, and beyond-accuracy, evaluation measures and splitting
techniques. However, the outstanding success and the community interest in Deep Learning
(DL) recommendation models, raised the need for novel instruments. LibRec [19], Spotlight 1 ,
and OpenRec [20] are the first open-source projects that made DL-based recommenders available
– with less than a dozen of available models but, unfortunately, without filtering, splitting, and
hyper-optimization tuning strategies. An important step towards more exhaustive and up-to-date
set of model implementations have been released with RecQ [21], DeepRec [22], and Cornac [23]
frameworks. However, they do not provide a general tool for extensive experiments on the
pre-elaboration and the evaluation of a dataset. Indeed, after the reproducibility hype [24, 25],
DaisyRec [14] and RecBole [26] raised the bar of framework capabilities, making available both
large set of models, data filtering/splitting operations and, above all, hyper-parameter tuning
features. Unfortunately, even though these frameworks are a great help to researchers, facilitating
reproducibility or extending the provided functionality would typically depend on developing
bash scripts or programming on whatever language each framework is written.
This is where Elliot comes to the stage. It is a novel kind of recommendation framework,
aimed to overcome these obstacles by proposing a fully declarative approach (by means of a
configuration file) to the set-up of an experimental setting. It analyzes the recommendation
problem from the researcher’s perspective as it implements the whole experimental pipeline,
from dataset loading to results gathering in a principled way. The main idea behind Elliot is to
keep an entire experiment reproducible and put the user (in our case, a researcher or RS developer)
in control of the framework. To date, according to the recommendation model, Elliot allows for
choosing among 27 similarity metrics, defining of multiple neural architectures, and choosing
51 hyperparameter tuning combined approaches, unleashing the full potential of the HyperOpt
library [27]. To enable evaluation for the diverse tasks and domains, Elliot supplies 36 metrics
(including Accuracy, Error-based, Coverage, Novelty, Diversity, Bias, and Fairness metrics), 13
splitting strategies, and 8 prefiltering policies.
1
https://github.com/maciejkula/spotlight
� Configuration File
Prefiltering Loading Data Modules
Filter-by-rating Ratings Run Module
k-core Side Information Evaluation Modules
Output Module
Optional Modules
Splitting Recommendation
Temporal
Random Output
Hyperoptimization
Restore Model Metrics
Fix Performance Tables
Accuracy
Model Weights
Error
Restore Model Recommendation Lists
Coverage
Novelty
Diversity Stat. Tests
Restore External Model Bias Paired t-test
Fairness Wilcoxon
Figure 1: Overview of Elliot.
2. Framework
Elliot is an extendable framework made up of eight functional modules, each of which is in charge
of a different phase in the experimental suggestion process. The user is only required to submit
human-level experimental flow information via a customisable configuration file, so what happens
beneath the hood (Figure 1) is transparent to them. As a result, Elliot constructs the whole
pipeline. What follows presents each of Elliot’s modules and how to create a configuration file.
2.1. Data Preparation
The Data modules are in charge of handling and organizing the experiment’s input, as well as
providing a variety of supplementary data, such as item characteristics, visual embeddings, and
pictures. The input data is taken over by the Prefiltering and Splitting modules after being loaded
by the Loading module, whose techniques are described in Sect.2.1.2 and 2.1.3 respectively.
2.1.1. Loading
Different data sources, such as user-item feedback or side information, such as the item visual
aspects, may be required for RSs investigations. Elliot comes with a variety of Loading module im-
plementations to meet these requirements. Furthermore, the user may create computationally in-
tensive prefiltering and splitting operations that can be saved and loaded to save time in the future.
Additional data, such as visual characteristics and semantic features generated from knowledge
graphs, can be handled through data-driven extensions. When a side-information-aware Loading
module is selected, it filters out items that lack the needed information to provide a fair comparison.
2.1.2. Prefiltering
Elliot provides data filtering procedures using two different techniques after data loading.
Filter-by-rating is the first method implemented in the Prefiltering module, which removes a user-
item interaction if the preference score falls below a certain level. It can be a Numerical value, such
as 3.5, a Distributional information, such as the worldwide rating average value, or a user-based
�distributional (User Dist.) value, such as the user’s average rating value. The 𝑘-core prefiltering
approach eliminates people, objects, or both if there are less than 𝑘 documented interactions. The
𝑘-core technique can be used to both users and things repeatedly (Iterative 𝑘-core) until the 𝑘-core
filtering requirement is fulfilled, i.e., all users and items have at least 𝑘 recorded interactions. Since
reaching such condition might be intractable, Elliot allows specifying the maximum number of
iterations (Iter-𝑛-rounds). Finally, the Cold-Users filtering feature allows retaining cold-users only.
2.1.3. Splitting
Elliot implements three splitting strategies: (i) Temporal, (ii) Random, and (iii) Fix. The
Temporal method divides user-item interactions depending on the transaction timestamp, either
by setting the timestamp, selecting the best one [28, 29], or using a hold-out (HO) mechanism.
Hold-out (HO), 𝐾-repeated hold-out (K-HO), and cross-validation (CV ) are all part of the Random
methods. Finally, the Fix approach leverages an already split dataset.
2.2. Recommendation Models
After data loading and pre-elaborations, the Recommendation module (Figure 1) provides the
functionalities to train (and restore) both Elliot’s state-of-the-art recommendation models and
custom user-implemented models, with the possibility to find the best hyper-parameter setting.
2.2.1. Implemented Models
To date, Elliot integrates around 50 recommendation models grouped into two sets: (i) popular
models implemented in at least two of the other reviewed frameworks, and (ii) other well-
known state-of-the-art recommendation models which are less common in the reviewed frame-
works, such as autoencoder-based, e.g., [6], graph-based, e.g., [30], visually-aware [31], e.g., [32],
adversarially-robust, e.g., [33], and content-aware, e.g., [34, 35].
2.2.2. Hyper-parameter Tuning
According to Rendle et al. [25], Anelli et al. [36], hyper-parameter optimization has a significant
impact on performance. Elliot supplies Grid Search, Simulated Annealing, Bayesian Optimization,
and Random Search, supporting four different traversal techniques in the search space. Grid
Search is automatically inferred when the user specifies the available hyper-parameters.
2.3. Performance Evaluation
After the training phase, Elliot continues its operations, evaluating recommendations. Figure 1
indicates this phase with two distinct evaluation modules: Metrics and Statistical Tests.
�2.3.1. Metrics
Elliot provides a set of 36 evaluation metrics, partitioned into seven families: Accuracy, Error,
Coverage, Novelty, Diversity, Bias, and Fairness. It is worth mentioning that Elliot is the framework
that exposes both the largest number of metrics and the only one considering bias and fairness mea-
sures. Moreover, the practitioner can choose any metric to drive the model selection and the tuning.
2.3.2. Statistical Tests
All other cited frameworks do not support statistical hypothesis tests, probably due to the need
for computing fine-grained (e.g., per-user or per-partition) results and retaining them for each
recommendation model. Conversely, Elliot helps computing two statistical hypothesis tests,
i.e., Wilcoxon and Paired t-test, with a flag in the configuration file.
2.4. Framework Outcomes
When the training of recommenders is over, Elliot uses the Output module to gather the results.
Three types of output files can be generated: (i) Performance Tables, (ii) Model Weights, and (iii)
Recommendation Lists. Performance Tables come in the form of spreadsheets, including all the
metric values generated on the test set for each recommendation model given in the configuration
file. Cut-off-specific and model-specific tables are included in a final report (i.e., considering each
combination of the explored parameters). Statistical hypothesis tests are also presented in the
tables, as well as a JSON file that summarizes the optimal model parameters. Optionally, Elliot
stores the model weights for the sake of future re-training.
2.5. Preparation of the Experiment
Elliot is triggered by a single configuration file written in YAML (e.g., refer to the toy ex-
ample sample_hello_world.yml). The first section details the data loading, filtering, and
splitting information defined in Section 2.1. The models section represents the recommendation
models’ configuration, e.g., Item-𝑘NN. Here, the model-specific hyperparameter optimization
strategies are specified, e.g., the grid-search. The evaluation section details the evaluation
strategy with the desired metrics, e.g., nDCG in the toy example. Finally, save_recs and top_k
keys detail, for example, the Output module abilities described in Section 2.4.
3. Conclusion and Future Work
Elliot is a framework that looks at the recommendation process from the eyes of an RS
researcher. To undertake a thorough and repeatable experimental assessment, the user only has
to generate a flexible configuration file. Several loading, prefiltering, splitting, hyperparameter
optimization, recommendation models, and statistical hypothesis testing are included in the
framework. Elliot reports may be evaluated and used directly into research papers. We eval-
uated the RS assessment literature, putting Elliot in the context of the other frameworks and
highlighted its benefits and drawbacks. Following that, we looked at the framework’s design
�and how to create a functional (and repeatable) experimental benchmark. Elliot is the only
recommendation framework we’re aware of that supports a full multi-recommender experimen-
tal pipeline from a single configuration file. We intend to expand the framework in the near
future to incorporate sequential recommendation scenarios, adversarial attacks, reinforcement
learning-based recommendation systems, differential privacy facilities, sampling assessment,
and distributed recommendation, among other things.
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�
The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models[edit]
The Challenging Reproducibility
Task in Recommender Systems Research
between Traditional and Deep Learning Models
Discussion Paper
Vito Walter Anelli1 ,
Alejandro Bellogín2 , Antonio Ferrara1 , Daniele Malitesta1 , Felice Antonio Merra4 ,
Claudio Pomo1 , Francesco Maria Donini3 , Eugenio Di Sciascio1 and Tommaso Di Noia1
1
Politecnico di Bari, via Orabona, 4, 70125 Bari, Italy
2
Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
3
Università degli Studi della Tuscia, via Santa Maria in Gradi, 4, 01100 Viterbo, Italy
4
Amazon Science Berlin, Invalidenstraße 75, 10557 Berlin, Germany
Abstract
Recommender Systems have shown to be a useful tool for reducing over-choice and providing accurate,
personalized suggestions. The large variety of available recommendation algorithms, splitting techniques,
assessment protocols, metrics, and tasks, on the other hand, has made thorough experimental evaluation
extremely difficult. Elliot is a comprehensive framework for recommendation with the goal of running and
reproducing a whole experimental pipeline from a single configuration file. The framework uses a variety
of ways to load, filter, and divide data. Elliot optimizes hyper-parameters for a variety of recommendation
algorithms, then chooses the best models, compares them to baselines, computes metrics ranging from
accuracy to beyond-accuracy, bias, and fairness, and does statistical analysis. The aim is to provide
researchers with a tool to ease all the experimental evaluation phases (and make them reproducible), from
data reading to results collection. Elliot is freely available on GitHub at https://github.com/sisinflab/elliot.
Keywords
Recommender Systems, Reproducibility, Adversarial Learning, Visual Recommenders, Knowledge Graphs
1. Introduction
In the last decade, Recommendation Systems (RSs) have gained momentum as the pivotal choice
for personalized decision-support systems. Recommendation is essentially a retrieval task where
a catalog of items is ranked in a personalized way and the top-scoring items are presented to the
user [1]. Once the RSs ability to provide personalized items to clients had been demonstrated,
both academia and industry began to devote attention to them [2]. This collective effort resulted
in an impressive number of recommendation algorithms, ranging from memory-based [3] to
latent factor-based [4, 5], as well as deep learning-based methods [6]. At the same time, the RS
research community realized that focusing only on the accuracy of results could be detrimental,
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ vitowalter.anelli@poliba.it (V. W. Anelli); claudio.pomo@poliba.it (C. Pomo)
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�and started exploring beyond-accuracy evaluation [7]. As accuracy was recognized as insufficient
to guarantee users’ satisfaction [8], novelty and diversity [9, 10] came into play as new dimensions
to be analyzed when comparing algorithms. However, this was only the first step in the direction
of a more comprehensive evaluation. Indeed, more recently, the presence of biased [11] and unfair
recommendations towards user groups and item categories [12] has been widely investigated.
The abundance of possible choices has generated confusion around choosing the correct base-
lines, conducting the hyperparameter optimization and the experimental evaluation [13], and
reporting the details of the adopted procedure. Consequently, two major concerns have arisen:
unreproducible evaluation and unfair comparisons [14].
The advent of various frameworks over the last decade has improved the research process, and
the RS community has gradually embraced the emergence of recommendation, assessment, and
even hyperparameter tweaking frameworks. Starting from 2011, Mymedialite [15], LensKit [16],
LightFM [17], RankSys [9], and Surprise [18], have formed the basic software for rapid prototyp-
ing and testing of recommendation models, thanks to an easy-to-use model execution and the
implementation of standard accuracy, and beyond-accuracy, evaluation measures and splitting
techniques. However, the outstanding success and the community interest in Deep Learning
(DL) recommendation models, raised the need for novel instruments. LibRec [19], Spotlight 1 ,
and OpenRec [20] are the first open-source projects that made DL-based recommenders available
– with less than a dozen of available models but, unfortunately, without filtering, splitting, and
hyper-optimization tuning strategies. An important step towards more exhaustive and up-to-date
set of model implementations have been released with RecQ [21], DeepRec [22], and Cornac [23]
frameworks. However, they do not provide a general tool for extensive experiments on the
pre-elaboration and the evaluation of a dataset. Indeed, after the reproducibility hype [24, 25],
DaisyRec [14] and RecBole [26] raised the bar of framework capabilities, making available both
large set of models, data filtering/splitting operations and, above all, hyper-parameter tuning
features. Unfortunately, even though these frameworks are a great help to researchers, facilitating
reproducibility or extending the provided functionality would typically depend on developing
bash scripts or programming on whatever language each framework is written.
This is where Elliot comes to the stage. It is a novel kind of recommendation framework,
aimed to overcome these obstacles by proposing a fully declarative approach (by means of a
configuration file) to the set-up of an experimental setting. It analyzes the recommendation
problem from the researcher’s perspective as it implements the whole experimental pipeline,
from dataset loading to results gathering in a principled way. The main idea behind Elliot is to
keep an entire experiment reproducible and put the user (in our case, a researcher or RS developer)
in control of the framework. To date, according to the recommendation model, Elliot allows for
choosing among 27 similarity metrics, defining of multiple neural architectures, and choosing
51 hyperparameter tuning combined approaches, unleashing the full potential of the HyperOpt
library [27]. To enable evaluation for the diverse tasks and domains, Elliot supplies 36 metrics
(including Accuracy, Error-based, Coverage, Novelty, Diversity, Bias, and Fairness metrics), 13
splitting strategies, and 8 prefiltering policies.
1
https://github.com/maciejkula/spotlight
� Configuration File
Prefiltering Loading Data Modules
Filter-by-rating Ratings Run Module
k-core Side Information Evaluation Modules
Output Module
Optional Modules
Splitting Recommendation
Temporal
Random Output
Hyperoptimization
Restore Model Metrics
Fix Performance Tables
Accuracy
Model Weights
Error
Restore Model Recommendation Lists
Coverage
Novelty
Diversity Stat. Tests
Restore External Model Bias Paired t-test
Fairness Wilcoxon
Figure 1: Overview of Elliot.
2. Framework
Elliot is an extendable framework made up of eight functional modules, each of which is in charge
of a different phase in the experimental suggestion process. The user is only required to submit
human-level experimental flow information via a customisable configuration file, so what happens
beneath the hood (Figure 1) is transparent to them. As a result, Elliot constructs the whole
pipeline. What follows presents each of Elliot’s modules and how to create a configuration file.
2.1. Data Preparation
The Data modules are in charge of handling and organizing the experiment’s input, as well as
providing a variety of supplementary data, such as item characteristics, visual embeddings, and
pictures. The input data is taken over by the Prefiltering and Splitting modules after being loaded
by the Loading module, whose techniques are described in Sect.2.1.2 and 2.1.3 respectively.
2.1.1. Loading
Different data sources, such as user-item feedback or side information, such as the item visual
aspects, may be required for RSs investigations. Elliot comes with a variety of Loading module im-
plementations to meet these requirements. Furthermore, the user may create computationally in-
tensive prefiltering and splitting operations that can be saved and loaded to save time in the future.
Additional data, such as visual characteristics and semantic features generated from knowledge
graphs, can be handled through data-driven extensions. When a side-information-aware Loading
module is selected, it filters out items that lack the needed information to provide a fair comparison.
2.1.2. Prefiltering
Elliot provides data filtering procedures using two different techniques after data loading.
Filter-by-rating is the first method implemented in the Prefiltering module, which removes a user-
item interaction if the preference score falls below a certain level. It can be a Numerical value, such
as 3.5, a Distributional information, such as the worldwide rating average value, or a user-based
�distributional (User Dist.) value, such as the user’s average rating value. The 𝑘-core prefiltering
approach eliminates people, objects, or both if there are less than 𝑘 documented interactions. The
𝑘-core technique can be used to both users and things repeatedly (Iterative 𝑘-core) until the 𝑘-core
filtering requirement is fulfilled, i.e., all users and items have at least 𝑘 recorded interactions. Since
reaching such condition might be intractable, Elliot allows specifying the maximum number of
iterations (Iter-𝑛-rounds). Finally, the Cold-Users filtering feature allows retaining cold-users only.
2.1.3. Splitting
Elliot implements three splitting strategies: (i) Temporal, (ii) Random, and (iii) Fix. The
Temporal method divides user-item interactions depending on the transaction timestamp, either
by setting the timestamp, selecting the best one [28, 29], or using a hold-out (HO) mechanism.
Hold-out (HO), 𝐾-repeated hold-out (K-HO), and cross-validation (CV ) are all part of the Random
methods. Finally, the Fix approach leverages an already split dataset.
2.2. Recommendation Models
After data loading and pre-elaborations, the Recommendation module (Figure 1) provides the
functionalities to train (and restore) both Elliot’s state-of-the-art recommendation models and
custom user-implemented models, with the possibility to find the best hyper-parameter setting.
2.2.1. Implemented Models
To date, Elliot integrates around 50 recommendation models grouped into two sets: (i) popular
models implemented in at least two of the other reviewed frameworks, and (ii) other well-
known state-of-the-art recommendation models which are less common in the reviewed frame-
works, such as autoencoder-based, e.g., [6], graph-based, e.g., [30], visually-aware [31], e.g., [32],
adversarially-robust, e.g., [33], and content-aware, e.g., [34, 35].
2.2.2. Hyper-parameter Tuning
According to Rendle et al. [25], Anelli et al. [36], hyper-parameter optimization has a significant
impact on performance. Elliot supplies Grid Search, Simulated Annealing, Bayesian Optimization,
and Random Search, supporting four different traversal techniques in the search space. Grid
Search is automatically inferred when the user specifies the available hyper-parameters.
2.3. Performance Evaluation
After the training phase, Elliot continues its operations, evaluating recommendations. Figure 1
indicates this phase with two distinct evaluation modules: Metrics and Statistical Tests.
�2.3.1. Metrics
Elliot provides a set of 36 evaluation metrics, partitioned into seven families: Accuracy, Error,
Coverage, Novelty, Diversity, Bias, and Fairness. It is worth mentioning that Elliot is the framework
that exposes both the largest number of metrics and the only one considering bias and fairness mea-
sures. Moreover, the practitioner can choose any metric to drive the model selection and the tuning.
2.3.2. Statistical Tests
All other cited frameworks do not support statistical hypothesis tests, probably due to the need
for computing fine-grained (e.g., per-user or per-partition) results and retaining them for each
recommendation model. Conversely, Elliot helps computing two statistical hypothesis tests,
i.e., Wilcoxon and Paired t-test, with a flag in the configuration file.
2.4. Framework Outcomes
When the training of recommenders is over, Elliot uses the Output module to gather the results.
Three types of output files can be generated: (i) Performance Tables, (ii) Model Weights, and (iii)
Recommendation Lists. Performance Tables come in the form of spreadsheets, including all the
metric values generated on the test set for each recommendation model given in the configuration
file. Cut-off-specific and model-specific tables are included in a final report (i.e., considering each
combination of the explored parameters). Statistical hypothesis tests are also presented in the
tables, as well as a JSON file that summarizes the optimal model parameters. Optionally, Elliot
stores the model weights for the sake of future re-training.
2.5. Preparation of the Experiment
Elliot is triggered by a single configuration file written in YAML (e.g., refer to the toy ex-
ample sample_hello_world.yml). The first section details the data loading, filtering, and
splitting information defined in Section 2.1. The models section represents the recommendation
models’ configuration, e.g., Item-𝑘NN. Here, the model-specific hyperparameter optimization
strategies are specified, e.g., the grid-search. The evaluation section details the evaluation
strategy with the desired metrics, e.g., nDCG in the toy example. Finally, save_recs and top_k
keys detail, for example, the Output module abilities described in Section 2.4.
3. Conclusion and Future Work
Elliot is a framework that looks at the recommendation process from the eyes of an RS
researcher. To undertake a thorough and repeatable experimental assessment, the user only has
to generate a flexible configuration file. Several loading, prefiltering, splitting, hyperparameter
optimization, recommendation models, and statistical hypothesis testing are included in the
framework. Elliot reports may be evaluated and used directly into research papers. We eval-
uated the RS assessment literature, putting Elliot in the context of the other frameworks and
highlighted its benefits and drawbacks. Following that, we looked at the framework’s design
�and how to create a functional (and repeatable) experimental benchmark. Elliot is the only
recommendation framework we’re aware of that supports a full multi-recommender experimen-
tal pipeline from a single configuration file. We intend to expand the framework in the near
future to incorporate sequential recommendation scenarios, adversarial attacks, reinforcement
learning-based recommendation systems, differential privacy facilities, sampling assessment,
and distributed recommendation, among other things.
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