Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/queirozfcom/recommender-systems
https://github.com/queirozfcom/recommender-systems
recommender-system
Last synced: 4 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/queirozfcom/recommender-systems
- Owner: queirozfcom
- License: mit
- Created: 2015-09-27T04:39:29.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T21:08:13.000Z (over 2 years ago)
- Last Synced: 2024-04-16T03:20:45.476Z (9 months ago)
- Topics: recommender-system
- Language: Jupyter Notebook
- Size: 22.5 MB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Recommender systems
Code for COS836 - Topics in Database Systems - Recommender Systems at COPPE/UFRJ
### List 1
- Located under `code/julia/list-1/`
### List 2
- Located under `code/julia/list-2/`
- ex 3,4:
- It seems that the average of the rating of one ITEM over all users that rated it performs slightly better (i.e. is a slightly better predictor for the actual rating) than the average of the rating of one USER over all items he/she rated:
- 5 runs of `ex3.jl` (using user average) averaged **0.833** error.
- 5 runs of `ex4.jl` (using item average) averaged **0.813** error.
- ex 5
- For exercise 5, I've experimented using *both* the average rating by the user *and* the average rating given for the item when predicting a rating given by a user for an item. Each "guess" was given equal weight
- 5 runs of `ex5.jl` averaged **0.795** error
### Item-based K-Nearest Neighbours Algorithm
- Located under `code/python/item-based-knn/`
> Based upon [Andre Ng's CS 229 notes](http://cs229.stanford.edu/proj2008/Wen-RecommendationSystemBasedOnCollaborativeFiltering.pdf)
`item-based-knn.py` is a naïve implementation of the k-nearest neighbours algorithm applied for recommending items.
The general idea is to find items that are similar to a movie *m* so that we can determine what rating a user *u* would give for movie *m*, given that user *u* has not yet seen movie *m*. I have used the **adjusted cosine similarity** between movies a and b.
Sample results can be found in file `results.csv`, which is of the form:
USER_ID,ITEM_ID,ORIGINAL_RATING,PREDICTED_RATING,ROOT_SQUARED_ERROR
### SVD Algorithm
- Located under `code/python/svd/`
This algorithm tries to decompose and discover a *low-dimensional* approximation of the **user-movie** matrix (**A**), where each row represents a user and each column represents a movie, and element **Aij** represents the *rating* user *i* gave to movie *j*.
Each user *u* and movie *m* have a **feature vector**, modelled in the same feature-space. Unknown ratings can be inferred by performing the dot product between the feature vectors for user *u* and movie *m*.
### Adding Temporal Features to COFILS method
Add temporal features to the method created by [Braida et al. (2015)](http://www.sciencedirect.com/science/article/pii/S095741741500038X). The COFILS method basically transforms a preference matrix (users X items) into a dataset that can be used for regular supervised learning, using matrix factorization techniques such as SVD (Singular-value decomposition).
We experiment with several ways of adding temporal features to the dataset obtained from the previous step, and explain results obtained after applying each strategy.
- Located under [code/python/tcofils/](https://github.com/queirozfcom/recommender-systems/tree/master/code/python)
- Executable file (entry point): `code/python/tcofils`
- [Link to presentation (in Portuguese)](https://docs.google.com/presentation/d/1quFFebU2GBVY23mU6wxP6WHqpQ8Ho5uuXnYpa2B5958/edit?usp=sharing)