Language used : Python 2.7
Dataset used : MovieLens 100k, available at http://grouplens.org/datasets/movielens/
Features Implemented :
- User Based Recommendation
- Item Based Recommendation
How to use it:
Step 1: Import Recommender Class
Step 2: Initialise the Recommendation System
Recommender function requires four arguments:
1.Rmin -> minimum rating in a rating scale
2.Rmax -> maximum rating in a rating scale
3.User Dataset
4.Item Dataset
Step 3a: For UserBased Recommendation System, UserRecommendation method is implemented
It requires 2 arguments:
1. UserID
2. Method for Similarity measures
Six different similarity measures are implemented:
1.Proximity-Impact-Popularity (PIP) measure, as proposed by Hyung Jun Ahn in "A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem", available at
http://www.sciencedirect.com/science/article/pii/S0020025507003751
2.Euclidean distance
3.Pearson’s Correlation (COR)
4.Cosine (Cos)
5.Constrained Pearson’s Correlation(CPC)
6.Spearman’s Rank Correlation (SRC)
Step 3b: For ItemBased Recommendation System, ItemRecommendation method is implemented
It takes 3 arguments:
1. UserID
2. Method for Similarity measures
3. Top n matches required