In this project, we compared the results of different recommendation systems using the recommenderlab library based on the MovieLens Small dataset.
The recommendation systems used are based on two main types:
- UBCF (User Based Collaborative Filtering)
- IBCF (Item Based Collaborative Filtering)
trained on the same dataset, but separated with different techniques:
- Split
- Bootstrap (sampling with replacement)
- Cross Validation
with different comparison measures between items:
- Cosine Similarity
- Pearson Correlation
and with different measures for normalizing items:
- Center
- Z-Score
The aforementioned recommendation systems were also trained on a dataset where the average ratings are weighted using the timestamps of individual ratings.
Additionally, a binary recommendation system was implemented, where the values are converted from the scale
The complete report, which includes a comprehensive analysis of the data and a detailed explanation of each recommendation system, is available as a PDF inside the repository. The report is written in Italian.