V ponedeljek, 8. septembra 2025, bodo ob 16:00 uri izvedena tri 
predavanja v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 8. september 2025 ob 16.00 prek Zoom-a (https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09).

1. predavanje:
============

-------------------------------------------------------
PREDAVATELJ: Arsen Matej GOLUBOVIKJ
-------------------------------------------------------

Arsen Matej Golubovikj is currently an assistant and a PhD student in HICUP Lab at UP FAMNIT, under the supervision of Marko Tkalčič. His academic interests encompass recommender systems, and user modeling, primarily in subjective domains, such as music recommendation and movie recommendation. His doctoral work explores user-centric recommender systems that account for subjective item perspectives.

-------------------------------------------------------------------------------------------------------------------------------------------------
NASLOV: Intended Movie Experience: Linking Elicited Emotions to Eudaimonic and Hedonic Characteristics
-------------------------------------------------------------------------------------------------------------------------------------------------

POVZETEK:

This study investigates the relationship between movies’ elicited emotions and their eudaimonic (meaningful) and hedonic (pleasurable) characteristics. We use emotional signatures derived from movie reviews, which have been previously shown to capture these elicited emotions. We examine correlations with both the movies’ eudaimonic and hedonic characteristics and the users’ eudaimonic and hedonic orientations, calculated based on their highly rated movies. We demonstrate the predictive power of emotional signatures in determining both movies’ and users’ experiential qualities and assess how genre clusters differ in their eudaimonic and hedonic characteristics based on these signatures. To the best of our knowledge, this is the first study to explore these connections. Ultimately, our findings aim to enhance personalized recommender systems by aligning recommendations with users’ emotional needs and desired experiences.

Seminar bo potekal v angleškem jeziku.

==============================================================================

2. predavanje:
============

--------------------------------------
PREDAVATELJ: Uroš SERGAŠ
--------------------------------------

Uroš Sergaš is a teaching assistant of Computer Science at FAMNIT. As a researcher specializing in recommender systems and computational social science, his work focuses on applying machine learning methods to address societal challenges. Recently, his work also addresses the issue of AI alignment. He is currently pursuing his PhD in recommender systems.

-----------------------------------------------------------------------------------------
NASLOV: News Recommender System for Political Depolarization
-----------------------------------------------------------------------------------------

POVZETEK:

The proposed work aims to explore the effectiveness of recommendation system methods in addressing the societal issue of political polarization. By promoting exposure to diverse stances on various social issues, we intend, through machine learning methods, to reduce polarization among individuals. To this end, we will use and compare methods of labelling and categorizing news article texts based on the presence of polarizing content. This will be followed by the development of a model to measure the range of stances that individuals perceive as acceptable, neutral, or unacceptable, including radical perspectives. The final step of the proposed work will involve reviewing various news recommendation strategies aimed at depolarization. We will conduct a study where, through regular individual interaction with a news reader and questionnaires, we will measure the impact of recommendation strategies on reducing political polarization. News items will be categorized within the reader based on the individual's receptiveness to alternative stances.

Seminar bo potekal v angleškem jeziku.

==============================================================================

3. predavanje:
============

----------------------------------------------------------------
PREDAVATELJICA: Kosar SEYYEDHOSSEINZADEH
----------------------------------------------------------------

Kosar Seyyedhosseinzadeh is a PhD student in HICUP lab at UP FAMNIT under the supervision of Prof. Marko Tkalčič. Her research focuses on recommender systems and user modeling for personalized experiential media.

-------------------------------------------------------------------------------------------------------------------------------------------------
NASLOV: Leveraging social influence based on users' activity centers for point-of-interest recommendation
-------------------------------------------------------------------------------------------------------------------------------------------------

POVZETEK:

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users’ geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on the Gowalla and Yelp datasets.

Seminar bo potekal v angleškem jeziku.

==============================================================================

Seminarji bodo izvedeni online prek Zoom-a na sledeči povezavi:

https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09

Meeting ID: 297 328 207
Passcode: 123456789

Vabljeni!