Thesis offers

MAI

MARIA SALAMÓ

UB

Most of the research on group recommendation investigated the core algorithms used for recommendation generation.  Two different strategies have been mostly used for generating group recommendations: aggregating individual predictions into group predictions or aggregating individual models into group models. Differences among these strategies differ in the timing of data aggregation step. 

 

In fact, the role of a group recommender system is to make suggestions that reflect the preferences of the group as a whole, while offering reasonable and acceptable options to individual group members. An important issue to be addressed in this kind of recommenders is how to reach consensus among members during and at the end of the recommendation process.

In this proposal, the focus will be on generating a group recommender framework that will be based on the well-known LibRec library. The main steps will be: 

1. Adapt Librec to be a GroupLibRec library 
2. Include in GroupLibRec the standard methods for generating group recommendations 
3. Propose new methods for generating group recommendations 
4. Analyze the proposals against well-known state-of-the-art methods

Note: Students interested in the proposal contact with the supervisor to an interview 

Requirements: strong motivation and good programming skills

MAI

ENRIQUE ROMERO MERINO

CS

Boltzmann Machines are probabilistic models developed in 1985 by
D.H. Ackley, G.E. Hinton and T.J. Sejnowski. In 2006, Restricted
Boltzmann Machines (RBMs) were used in the pre-training step of
several successful deep learning models, leading to a new renaissance
of neural networks and artificial intelligence.

In spite of their nice mathematical formulation, there are a number of
issues that are hard to compute:

1. The computation of the partition function is NP-hard, involving an  exponential sum of terms
2. The exact computation of the derivative of the log-likelihood is  also NP-hard, since it contains the derivative of the partition function

Therefore, in practice we have to approximate both the computation of
the probabilities and several components of the learning process
itself. These drawbacks have prevented RBMs to show their real
potential as truly probabilistic models.

Currently, we are working on trying to improve several of the unsolved
issues related to RBMs:

1. - Mechanisms to control the learning process: www.lsi.upc.edu/~eromero/Publications/Downloads/2018-tnnls-stopcritRBM.pdf
2. Better approximations of the derivative of the log-likelihood: http://www.lsi.upc.edu/~eromero/Publications/Downloads/2019-nn-weightedCD.pdf
3. Efficient approximation of the partition function (work in progress)

These works have opened new lines of research, some of which can be
the topic of a Master's Thesis. The scope and degree of depth of the
work can be adapted to the estimated times to complete the Thesis. For
further details, contact Enrique Romero (eromero@cs.upc.edu).

Good programming skills.

MAI

MIQUEL SANCHEZ MARRE

CS

Detailed Description: This work will be done within the current hot-topic of fostering circular economy in the chemical process industries. Circular economy is based on the management of waste through the 3R (reduce, recycle, reuse) transforming the traditional productive cycle (resource-product-waste) into a circular flow (resource-product-recycled resource) thus reducing resources and waste, in line with industrial ecology field.

The main goal of the work will be to design and develop a prototype of a recommender system based on the use, exploitation and extension of the OntoCAPE ontology  for a decision-making support tool in the design of alternative processes closing material loops in chemical supply chains.

A first task will be to understand the OntoCAPE ontology, being able to manipulate it extracting knowledge, using reasoning mechanisms, and possible, being able to add new information/knowledge to the ontology.

The second task will be to use the OntoCAPE ontology for helping the end-users to solve the following scenario: given some waste products, the recommender system should be able to suggest good combinations of processes, which may include waste material separations (to recover useful materials) but also more complex chemical transformations of waste to generate other materials, that can be used as resource materials in other processes. This combination of possible processes and materials is really high and it is very difficult to assess which are the best possible solutions regarding the minimization of economic costs and minimizing the ecological print.

A third task will consist on the design and implementation of interfaces with other software systems like process simulation and mathematical optimization software systems (e.g.: ASPEN, GAMS). These systems are extensively used for the analysis of processes systems and supply chains, with the aim of thoroughly evaluating and optimizing already identified transformation paths from economic, environmental, social and technical maturity points of view.

Additional information:

• https://en.wikipedia.org/wiki/OntoCAPE
• https://www.avt.rwth-aachen.de/cms/AVT/Wirtschaft/SoftwareSimulation/~ipts/OntoCape
• https://www.springer.com/gp/book/9783642046544
• https://www.sciencedirect.com/science/article/abs/pii/S0952197606001199

Advisor/s of the work: the work will be advised by Prof. Antonio Espuña (antonio.espuna@upc.edu) from Dept. of Chemical Engineering and CEPIMA research group (Centre for Process and Environment Engineering, https://cepima.upc.edu/en) and by Dr. Miquel Sànchez-Marrè (miquel@cs.upc.edu), from Dept. of Computer Science and IDEAI (Intelligent Data Science and Artificial Intelligence, https://ideai.upc.edu/en) Research Centre.

 Funding: the work will be a three-month funded during March-May 2021, within the framework of a Research project on Circular Economy in Process Engineering (CEPI), through a Research Support Personal category position at UPC (PSR, "Personal de Suport a la Recerca") starting as soon as possible.

The remuneration will be according to the dedication load of the candidates.  

 Contact: Antonio Espuña (antonio.espuna@upc.edu) and

                Miquel Sànchez-Marrè (miquel@cs.upc.edu).

 Deadline: March 7, 2021

Basic Requirements: The students should own a Graduate in Computer Science or equivalent, and they should be enrolled in the Master of Artificial Intelligence (UPC-URV-UB) or equivalent, being able to register and matriculate the TFM for the Fall Term of course 2020/2021.

Other requirements: Good oral and writing skills of English language. Additional Catalan and/or Spanish language skills can be positively evaluated.

Load work: This work will have a workload-equivalent of 3 months (450h) to generate a prototype. This work is the equivalent to the workload of a Final Project of a Master/Master's Thesis (TFM). Notwithstanding, the work can be spread along the year 2021 until the Fall term when the TFM must be presented.

MAI

ALFREDO VELLIDO ALCACENA y ENRIQUE ROMERO MERINO

CS

A more detailed description of the project can be found in

https://www.cs.upc.edu/~eromero/Downloads/Retina-TFM-Project-01.pdf

The project is proposed in collaboration with Javier Zarranz Ventura
(Institut Clínic d'Oftalmologia, ICOF, Hospital Clínic de Barcelona, and
Institut d'Investigacions Biomèdiques August Pi I Sunyer, IDIBAPS),
which would provide a large annotated database to develop the project. For further information, please contact Alfredo Vellido (avellido@cs.
upc.edu) or Enrique Romero (eromero@cs.upc.edu).

MAI

MARIA SALAMÓ

UB

Online educational platforms are promising to play a primary role in mediating the success of individuals' careers. Hence, while building overlying content recommendation services, it becomes essential to ensure that learners are provided with equal learning opportunities, according to the platform values, context, and pedagogy. Even though the importance of creating equality of learning opportunities has been well investigated in traditional institutions, how it can be operationalized scalably in online learning ecosystems through recommender systems is still under-explored.

 

The goal of this project is to analyze state-of-the-art metrics[1] that combines them to quantify the equality of learning opportunities among learners and mitigate the inequalities generated by the recommender systems as post-processed approach that balances personalization and learning opportunity equality in recommendations. To do it, we will consider state-of-the-art recommender systems, covering both model- and memory -based approaches and point-wise and pair-wise algorithms.

 

[1] http://www.mirkomarras.com/publication/ijaied2020/

Note: Students interested in the proposal contact with the supervisor to an interview 

Requirements: Strong motivation and good programming skills

MAI

MARIA SALAMÓ

UB

Recommender systems analyze the behavior of the users and their preferences, to learn patterns and understand what might be interesting to suggest them[4]. Natural imbalances in the data (e.g., in the amount of observations for a subset of popular items) might be embedded in the patterns. As a consequence, the produced recommendations exacerbate these imbalances, thus strengthening inequalities and generating biases [2]. When these imbalances are associated to sensitive attributes of the users (e.g., gender or race), this might have negative societal consequences, such as unfairness [3]. Unfairness might affect all the stakeholders involved in a recommender system, such as the users (when the minority receives systematically worse recommendations), or the content providers (when the items offered by a group of providers are exposed less than those of their counterpart) [1].

The goal of this project is to analyze and mitigate bias in recommender systems. To do it, we will consider state-of-the-art recommender systems, covering both model- and memory -based approaches and point-wise and pair-wise algorithms. 

[1] Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz AugustoPizzato. 2020. Multistakeholder recommendation: Survey and research directions.User Model. User-Adapt. Interact.30, 1 (2020), 127¿158. https://doi.org/10.1007/s11257-019-09256-1

 

[2] Ludovico Boratto, Gianni Fenu, and Mirko Marras. 2019. The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses.InAdvances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14-18, 2019, Proceedings, Part I(Lecture Notes in Computer Science, Vol. 11437), Leif Azzopardi, Benno Stein, Norbert Fuhr, Philipp Mayr, Claudia Hauff, and Djoerd Hiemstra (Eds.).Springer, 457¿472. https://doi.org/10.1007/978-3-030-15712-8_30

 

[3] Sara Hajian, Francesco Bonchi, and Carlos Castillo. 2016. Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining.InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi (Eds.). ACM, 2125¿2126.https://doi.org/10.1145/2939672.2945386

 

[4] Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender Systems: Introduction and Challenges. InRecommender Systems Handbook,Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 1¿34. https://doi.org/10.1007/978-1-4899-7637-6_

Note: Students interested in the proposal contact with the supervisor to an interview 

Requirements: strong motivation and good programming skills