Rodrigo da Silva Alves

Welcome! Vítejte! Bem-vindo!
I am an Assistant Professor at the Department of Applied Mathematics
Faculty of Information Technology / Czech Technical University (FIT/CTU).

About me

Rodrigo da Silva Alves

Currently, I am an Assistant Professor (Recombee Lab's member) at the Department of Applied Mathematics, Faculty of Information Technology / Czech Technical University (FIT/CTU). My research focuses on recommender systems (theory and applications) and, more recently, sports analytics. However, I am interested in machine learning in general, especially related to data mining and how artificial intelligence correlates to other areas of computer science. Previously, I completed a Ph.D. in Computer Science at the Machine Learning Group under the supervision of Prof. Marius Kloft at TU Kaiserslautern , RP, Germany. I hold a Bachelor's degree in Information Systems and a Master's in Computer Science from the Department of Computer Science at the Federal University of Minas Gerais under the supervision of Prof. Renato Assunção. I am also a vocational educational teacher certificated by the Häme University of Applied Sciences, Finland, and I was a Lecturer from the Department of Applied Social Sciences at CEFET-MG. During my career, I had the pleasure of collaborating with various research groups (in different countries), outstanding students, and technical teams.

My hometown is Belo Horizonte (you can also say Beagá), Minas Gerais, Brazil. I am a Cruzeiro Esporte Clube fan and a enthusiast of Brazilian (multi-)culture and music, particularly Bossa Nova. I believe in the need of building an inclusive, accessible, multicultural, and prejudice-free environment for research.

Resume

Professional Experience

Assistant Professor (Odborný asistent)

Feb 2022 - present

Department of Applied Mathematics
Czech Technical University in Prague
Prague, Czech Republic

Researcher (Wissenschaftlicher Mitarbeiter)

Jan 2019 - Jan 2022

Machine Learning Group
Technical University of Kaiserslautern
Kaiserslautern, Rhineland-Palatinate, Germany

Lecturer (Professor EBTT)

Apr 2014 - Dec 2021 (On Ph.D. leave from Feb 2018)

Department of Applied Social Sciences
Centro Federal de Educação Tecnológica de Minas Gerais
Belo Horizonte, Minas Gerais, Brazil

Education

Dr. Rer. Nat - Computer Science

Feb 2018 - Feb 2022

Department of Computer Science
Technical University of Kaiserslautern
Kaiserslautern, Rhineland-Palatinate, Germany

Thesis: Towards Comprehensive Cluster-induced Methods for Recommender Systems
Supervisor: Prof. Marius Kloft

Professional Development Program for Teachers

Apr 2016 - Dec 2016

Häme University of Applied Sciences
Hämeenlinna, Finland

Development Work: Education for the Future: Applying Student-centered Learning in Brazilian Vocational Education
Supervisors: Dr. Essi Ryymin and Dr. Irma Kunnari
20 ECTS / 540 hours

Master of Computer Science

Feb 2013 - May 2015

Department of Computer Science
Federal University of Minas Gerais
Belo Horizonte, Minas Gerais, Brazil

Thesis: Stochastic point process mixing model for inter-event times of Web services
My Master's thesis is composed in Portuguese. Nevertheless, you may read this paper, which is an outcome of this research.
Supervisor: Prof. Renato Assunção

Bachelor of Information Systems

Feb 2009 - Dec 2012

Department of Computer Science
Federal University of Minas Gerais
Belo Horizonte, Minas Gerais, Brazil

Award: Best Student Award

News & Updates

[07/2023] 23rd European Agent Systems Summer School

Happy to announce my active participation in the 23rd European Agent Systems Summer School, hosted at the Faculty of Information Technology, Czech Technical University in Prague. For more comprehensive information, please visit the event's official website here. Additionally, you can access the slides from my presentation, which are now available here.

I am pleased to announce the publication of my latest academic paper on IEEE Transactions of Neural Networks and Learning Systems. The paper focuses on improving recommender systems (RSs) by addressing the issue of unequal amounts of noise in observed ratings. We propose a nuclear-norm-based matrix factorization method that leverages side information to estimate the uncertainty associated with each rating. By using this uncertainty as a weighting factor in our optimization process, we can effectively handle potentially erroneous or noisy ratings. For more details, click here.

We studied inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. The paper will appear in the AAAI 2023 proceeedings, but currently you can access here the arxiv version of the paper.

[01/2023] My website is online :)

Finally I managed to update my website. It is a working product version that will be updated time to time.

Teaching

Winter 23/24

[NIE-ML1] Machine Learning 1
[NI-PML] Personalised Machine Learning
[BI-SZ] Knowledge Engineering Seminar

Summer 22/23

[NIE-ADM] Data Mining Algorithms
[NI-ADM] Data Mining Algorithms
[NI-AML] Advanced Machine Learning

Winter 22/23

[BIE-VDZ] Data Mining
[BI-SZ] Knowledge Engineering Seminar

Summer 21/22

[NI-ADM] Data Mining Algorithms

Research

I am always open to collaborating on fascinating and challenging projects. If you are a CTU student and are looking for a Bachelor's or Master's project or would like to have your first steps in research, I would be glad to have a meeting with you and discuss the possibility of mentoring you in some projects in my research area. For internal and external collaboration, please get in touch with me by email. Below, you can find my main research contributions. Here is my google scholar profile.

Abstract: In a recommender systems (RSs) dataset, observed ratings are subject to unequal amounts of noise. Some users might be consistently more conscientious in choosing the ratings they provide for the content they consume. Some items may be very divisive and elicit highly noisy reviews. In this article, we perform a nuclear-norm-based matrix factorization method which relies on side information in the form of an estimate of the uncertainty of each rating. A rating with a higher uncertainty is considered more likely to be erroneous or subject to large amounts of noise, and therefore more likely to mislead the model. Our uncertainty estimate is used as a weighting factor in the loss we optimize. To maintain the favorable scaling and theoretical guarantees coming with nuclear norm regularization even in this weighted context, we introduce an adjusted version of the trace norm regularizer which takes the weights into account. This regularization strategy is inspired from the weighted trace norm which was introduced to tackle nonuniform sampling regimes in matrix completion. Our method exhibits state-of-the-art performance on both synthetic and real life datasets in terms of various performance measures, confirming that we have successfully used the auxiliary information extracted.
IEEE Transactions on Neural Networks and Learning Systems

Abstract: We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently from many works in approximate recovery, we present results both for bounded Lipschitz losses and for the absolute loss, with the latter relying on Talagrand-type inequalities. The proofs create a bridge between two approaches to the theoretical analysis of matrix completion, since they consist in a combination of techniques from both the exact recovery literature and the approximate recovery literature.
AAAI Conference on Artificial Intelligence

Abstract: Recently, the RS research community has witnessed a surge in popularity for shallow autoencoder-based CF methods. Due to its straightforward implementation and high accuracy on item retrieval metrics, EASE is potentially the most prominent of these models. Despite its accuracy and simplicity, EASE cannot be employed in some real-world recommender system applications due to its inability to scale to huge interaction matrices. In this paper, we proposed ELSA, a scalable shallow autoencoder method for implicit feedback recommenders. ELSA is a scalable autoencoder in which the hidden layer is factorizable into a low-rank plus sparse structure, thereby drastically lowering memory consumption and computation time. We conducted a comprehensive offline experimental section that combined synthetic and several real-world datasets. We also validated our strategy in an online setting by comparing ELSA to baselines in a live recommender system using an A/B test. Experiments demonstrate that ELSA is scalable and has competitive performance. Finally, we demonstrate the explainability of ELSA by illustrating the recovered latent space.
ACM Conference on Recommender Systems.

Abstract: In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in inductive matrix completion: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of $O(rd^2)$ to $\widetilde{O}(d^{3/2}\sqrt{r})$, where $d$ is the dimension of the side information and $r$ is the rank. (2) We introduce the (smoothed) adjusted trace-norm minimization strategy, an inductive analogue of the weighted trace norm, for which we show guarantees of the order $\widetilde{O}(dr)$ under arbitrary sampling. In the inductive case, a similar rate was previously achieved only under uniform sampling and for exact recovery. Both our results align with the state of the art in the particular case of standard (non-inductive) matrix completion, where they are known to be tight up to log terms. Experiments further confirm that our strategy outperforms standard inductive matrix completion on various synthetic datasets and real problems, justifying its place as an important tool in the arsenal of methods for matrix completion using side information.
Advances in Neural Information Processing Systems

Abstract: A reasonable assumption in recommender systems is that the rows (users) and columns (items) of the rating matrix can be split into groups (communities) with the following property: each entry of the matrix is the sum of components corresponding to community behavior and a purely low-rank component corresponding to individual behavior. We investigate (1) whether such a structure is present in real-world datasets, (2) whether the knowledge of the existence of such structure alone can improve performance, without explicit information about the community memberships. To these ends, we formulate a joint optimization problem over all (completed matrix, set of communities) pairs based on a nuclear-norm regularizer which jointly encourages both low-rank solutions and the recovery of relevant communities. Since our optimization problem is non-convex and of combinatorial complexity, we propose a heuristic algorithm to solve it. Our algorithm alternatingly refines the user and item communities through a clustering step jointly supervised by nuclear-norm regularization. The algorithm is guaranteed to converge. We performed synthetic and real data experiments to confirm our hypothesis and evaluate the efficacy of our method at recovering the relevant communities. The results shows that our method is capable of retrieving such an underlying (community behaviour + continuous low-rank) structure with high accuracy if it is present.
PMLR: NeurIPS Workshop on Pre-registration in Machine Learning

Abstract: In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where each timestamp corresponds to a visit by a user and a corresponding decision regarding recommendation. The main novelty is that we model the reward distribution as a consequence of variations in the intensity of the activity, and thereby we assist the exploration/exploitation dilemma by exploring the temporal dynamics of the audience. To achieve this, we assume that the recommendation procedure can be split into two different states: the loyal and the curious state. We identify the current state by modelling the events as a mixture of two Poisson processes, one for each of the possible states. We further assume that the loyal audience is associated with a single stationary reward distribution, but each bursty period comes with its own reward distribution. We test our algorithm and compare it to several baselines in two strands of experiments: synthetic data simulations and real-world datasets. The results demonstrate that BMAB achieves competitive results when compared to state-of-the-art methods.
ACM Conference on Recommender Systems

Abstract: We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As particular cases of our framework, we present models that can incorporate user and item biases or community information in a joint and additive fashion. We analyze the performance of OMIC on several synthetic and real datasets. On synthetic datasets with a sliding scale of user bias relevance, we show that OMIC better adapts to different regimes than other methods. On real-life datasets containing user/items recommendations and relevant side information, we find that OMIC surpasses the state of the art, with the added benefit of greater interpretability.
IEEE Transactions on Neural Networks and Learning Systems

Abstract: Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physicochemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering.
ACS The journal of physical chemistry letters

Abstract: In the so-called Total Quality Era, it is necessary to implement standardized and recognized experimental procedures around the world. When testing laboratories are adapted to the requirements set forth in ISO/IEC 17025:2017 standard, the evaluation of results and the exchange of knowledge becomes easier and more dynamic. This adaptation can be simplified and accelerated through the use of a data management software. Thus, the objective of this work was to develop a platform for quality control of a chemical testing laboratory, focusing on compliance with managerial and technical requirements of ISO/IEC 17025:2017 standard. The developed software allows not only data recording, but also the comparison of the analysis results with limit values established by current legislation, guaranteeing greater reliability of the reports issued. The created prototype is useful in ensuring high efficiency of the activities of chemical testing laboratories, making the workflow faster and safer, aside from guaranteeing compliance with the requirements of ISO/IEC 17025:2017 standard.
Brazilian Journal of Analytical Chemistry

Abstract: The problem to accurately and parsimoniously characterize random series of events (RSEs) seen in the Web, such as Yelp reviews or Twitter hashtags, is not trivial. Reports found in the literature reveal two apparent conflicting visions of how RSEs should be modeled. From one side, the Poissonian processes, of which consecutive events follow each other at a relatively regular time and should not be correlated. On the other side, the self-exciting processes, which are able to generate bursts of correlated events. The existence of many and sometimes conflicting approaches to model RSEs is a consequence of the unpredictability of the aggregated dynamics of our individual and routine activities, which sometimes show simple patterns, but sometimes results in irregular rising and falling trends. In this paper we propose a parsimonious way to characterize general RSEs, namely the Burstiness Scale (BuSca) model. BuSca views each RSE as a mix of two independent process: a Poissonian and a self-exciting one. Here we describe a fast method to extract the two parameters of BuSca that, together, gives the burstiness scale ψ, which represents how much of the RSE is due to bursty and viral effects. We validated our method in eight diverse and large datasets containing real random series of events seen in Twitter, Yelp, e-mail conversations, Digg, and online forums. Results showed that, even using only two parameters, BuSca is able to accurately describe RSEs seen in these diverse systems, what can leverage many applications.
ACM International Conference on Knowledge Discovery and Data Mining

Abstract: With the advancement of information systems, means of communications are becoming cheaper, faster, and more available. Today, millions of people carrying smartphones or tablets are able to communicate practically any time and anywhere they want. They can access their e-mails, comment on weblogs, watch and post videos and photos (as well as comment on them), and make phone calls or text messages almost ubiquitously. Given this scenario, in this article, we tackle a fundamental aspect of this new era of communication: How the time intervals between communication events behave for different technologies and means of communications. Are there universal patterns for the Inter-Event Time Distribution (IED)? How do inter-event times behave differently among particular technologies? To answer these questions, we analyzed eight different datasets from real and modern communication data and found four well-defined patterns seen in all the eight datasets. Moreover, we propose the use of the Self-Feeding Process (SFP) to generate inter-event times between communications. The SFP is an extremely parsimonious point process that requires at most two parameters and is able to generate inter-event times with all the universal properties we observed in the data. We also show three potential applications of the SFP: as a framework to generate a synthetic dataset containing realistic communication events of any one of the analyzed means of communications, as a technique to detect anomalies, and as a building block for more specific models that aim to encompass the particularities seen in each of the analyzed systems.
ACM Transactions on Knowledge Discovery from Data

Contact

Location:

Room A-1354 / Building A, 13th floor
Thákurova 7
Prague 6 – Dejvice
160 00

Please do not hesitate to contact me. I am often in my office, and you can visit me without an appointment. However, I am also frequently busy, so if you want to make sure you can talk to me, send a message before. If you are a CTU student looking for projects, read about it here.