| dc.description.abstract | The objective of food recommender systems is to provide recipe recommendations that are both personally relevant and technically accurate. This thesis
assesses the predictive accuracy and user acceptance of two algorithmic approaches: Content-Based Filtering (CBF) and Collaborative Filtering (CF).
An online survey study with 100 participants (from India and the Netherlands; 132 initially recruited) was conducted to measure outcomes such as
perceived relevance, satisfaction, trust, and intention to reuse, while offline
experiments assessed predictive error (MAE, RMSE). A mixed-method evaluation was conducted.
The results indicate that CF achieved a lower predictive error than CBF
in the offline evaluation. Acceptance of the system was inconsistent in the
user study; 35% of participants indicated that they were highly likely to
reuse it, 30% that they were very likely to do so, and 12% that they were
unlikely to do so. Subgroup analyses revealed that user characteristics influenced perceptions. For instance, participants with stricter dietary preferences
(e.g., vegetarian/gluten-free personas) reported higher satisfaction with CBF,
whereas broader user groups preferred CF for its novelty and relevance.
These results indicate that no single algorithm is the most effective in terms of
all criteria. In general, CF exhibits superior predictive performance, whereas
CBF provides benefits in terms of transparency and dietary alignment. This
thesis emphasizes the complementary strengths of CBF and CF. It offers
insights for the development of more effective and reliable food recommender
systems by integrating algorithmic evaluation with user-centered feedback. | |
