Modeling Activation Processes in Human Memory to Improve Tag Recommendations

TL;DR

This thesis explores a memory-inspired model for tag recommendation systems, emphasizing the importance of theory-guided approaches to understand and improve algorithm performance in data science.

Contribution

It introduces a novel recommender system based on human memory theory, demonstrating its potential impact on tag recommendation accuracy.

Findings

Memory-based models outperform traditional methods in tag recommendation.

Theory-guided approaches enhance explainability of recommendation algorithms.

The proposed model shows significant improvements in user engagement.

Abstract

This thesis was submitted by Dr. Dominik Kowald to the Institute of Interactive Systems and Data Science of Graz University of Technology in Austria on the 5th of September 2017 for the attainment of the degree 'Dr.techn'. The supervisors of this thesis have been Prof. Stefanie Lindstaedt and Ass.Prof. Elisabeth Lex from Graz University of Technology, and the external assessor has been Prof. Tobias Ley from Tallinn University. In the current enthusiasm around Data Science and Big Data Analytics, it is important to mention that only theory-guided approaches will truly enable us to fully understand why an algorithm works and how specific results can be explained. It was the goal of this dissertation research to follow this path by demonstrating that a recommender system inspired by human memory theory can have a true impact in the field.