Python version: 2.7.15
Modules needed: joblib, sklearn and numpy
This folder contains the python scripts used to run the experiments of my coursework at
Centro Universitario FEI.
The work aims at exploiting cluster specialization in hybrid recommender systems.
Because of the coursework deadline, we only adopted user-based and item-based
collaborative filtering algorithms for recommendation and k-Means algorithm for
clustering.
Future research will explore other state-of-art recommender and clustering algorithms.
The coursework_diagram.pdf presents a diagram summarizing the coursework methodology.
The original dataset used to evaluate the proposed methodology was 1M MovieLens
Dataset (dataset folder).
Due to computational time, the Usage.py is modeled to use a smaller dataset, the
100k MovieLens Dataset (dataset_small folder).
More info about the datasets at: https://grouplens.org/datasets/movielens/
To run the entire experiment, just run the Usage.py script.
There you can find the order the scripts should be run and their respective arguments.
Although the scripts were written using parallel processing in order to exploit all the
computational resource available (multithread based, not cluster based), some scripts
might take a relatively long time to run due to the dataset size and disk I/O
operations.
Moreover, a considerable amount of data storage is needed (about 2.1 GB for the default
experiment configuration).
To adapt this code to different rating-based recommender datasets (user-id, item-id,
rating), you must modify the 01_data_split_function.py script in order to read and
sample your dataset properly and configure the experiments parameters as you please
(basic python knowledge is needed).
Furthermore, you might want to analyze the results generated after the standalone
models evaluation in order to define the hybrid model parameters based on the
standalone results.
To do so, you must modify the 07_compute_evaluate_hybrid.py (basic python
knowledge is needed).