| Study programme competencies |
|
Code
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Study programme competences
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| A27 |
CE27 - Compresión e dominio de fundamentos e técnicas básicas para a procura e o filtrado de información en grandes coleccións de datos. |
| B2 |
CB2 - Que os estudantes saiban aplicar os seus coñecementos ao seu traballo ou vocación dunha forma profesional e posúan as competencias que adoitan demostrarse por medio da elaboración e defensa de argumentos e a resolución de problemas dentro da súa área de estudo |
| B3 |
CB3 - Que os estudantes teñan a capacidade de reunir e interpretar datos relevantes (normalmente dentro da súa área de estudo) para emitir xuízos que inclúan unha reflexión sobre temas relevantes de índole social, científica ou ética |
| B4 |
CB4 - Que os estudantes poidan transmitir información, ideas, problemas e solucións a un público tanto especializado como non especializado |
| B7 |
CG2 - Elaborar adecuadamente e con certa orixinalidade composicións escritas ou argumentos motivados, redactar plans, proxectos de traballo, artigos científicos e formular hipóteses razoables. |
| B8 |
CG3 - Ser capaz de manter e estender formulacións teóricas fundadas para permitir a introdución e explotación de tecnoloxías novas e avanzadas no campo. |
| B9 |
CG4 - Capacidade para abordar con éxito todas as etapas dun proxecto de datos: exploración previa dos datos, preprocesado, análise, visualización e comunicación de resultados. |
| B10 |
CG5 - Ser capaz de traballar en equipo, especialmente de carácter multidisciplinar, e ser hábiles na xestión do tempo, persoas e toma de decisións. |
| C1 |
CT1 - Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida. |
| C4 |
CT4 - Valorar a importancia que ten a investigación, a innovación e o desenvolvemento tecnolóxico no avance socioeconómico e cultural da sociedade. |
| Learning aims |
| Learning outcomes |
Study programme competences |
| Know, understand and analyze the different recommendation models |
A27
|
B2
B3
B8
B9
|
C1
C4
|
| Know, understand and analyze the techniques for an efficient implementation of scalable recommendation systems |
A27
|
B4
B7
B10
|
|
| Know, understand and analyze the evaluation methodologies of recommendation systems |
A27
|
B4
B8
B9
|
C4
|
| Contents |
| Topic |
Sub-topic |
| Introduction |
Recommender Systems |
| Preferences elicitation |
Ratings, elicitation |
| Recommendation models |
Collaborative filtering , content and hybrid |
| Evaluation of recommendation systems |
Metrics and protocols |
| Advanced recommendation models |
Contextual, social and temporal |
| Interpretability, justification and risks |
User-to-user and Item-to-Item recommendations |
| Applications and domains |
Tasks and use cases |
| Planning |
| Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
| Laboratory practice |
B2 B9 B10 C1 |
15 |
60 |
75 |
| Guest lecture / keynote speech |
A27 B3 B8 C4 |
19 |
54 |
73 |
| Mixed objective/subjective test |
A27 B2 B3 B4 B7 B8 C4 |
2 |
0 |
2 |
| |
| Personalized attention |
|
0 |
|
0 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
|
Methodologies |
Description |
| Laboratory practice |
Classes dedicated to the development of practical work involving the resolution of complex problems, and the analysis and design of solutions that constitute a means for their resolution. This activity may require students to present their work orally. The work carried out by the students can be done individually or in work groups |
| Guest lecture / keynote speech |
Oral exposition complemented with the use of audiovisual media and the introduction of some questions directed to the students, with the purpose of transmitting knowledge and facilitating learning. In addition to the time of oral exposition by the professor, this formative activity requires the student to dedicate some time to prepare and review on their own the materials object of the class |
| Mixed objective/subjective test |
Final exam |
| Personalized attention |
|
Methodologies
|
| Laboratory practice |
|
| Description |
| Monitoring of the development of the practices in the reserved hours of laboratory and attention to the student in the necessary cases of problems of particular difficulty |
|
| Assessment |
|
Methodologies
|
Competencies |
Description
|
Qualification
|
| Mixed objective/subjective test |
A27 B2 B3 B4 B7 B8 C4 |
Final exam |
50 |
| Laboratory practice |
B2 B9 B10 C1 |
Evaluation of the student's assignments |
50 |
| |
|
Assessment comments |
It will be necessary to reach 40% of the score in each part. The evaluation will be considered as not presented when no practical work or final exam is not submitted. Second opportunity The evaluation will be carried out with the same criteria described above. A new deadline will be opened for the delivery of the practical works, in the event that they are not delivered at the first opportunity. |
| Sources of information |
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Basic
|
|
|
Ricci, F., Rokach, L., & Shapira, B. Recommender systems handbook. Springer, Boston, MA. Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press. Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing. Banik, R. (2018). Hands-on recommendation systems with Python: start building powerful and personalized, recommendation engines with Python. Packt Publishing Ltd. |
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Complementary
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| Recommendations |
| Subjects that it is recommended to have taken before |
| Information Retrieval/614G02027 | | Machine Learning I/614G02019 | | Linear Algebra/614G02001 |
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| Subjects that are recommended to be taken simultaneously |
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| Subjects that continue the syllabus |
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