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Recommendation systems are widely used in e-commerce companies like Amazon, Netix to help users discover items that they might not have found bythemselves.There are a number of techniques that are currently being employed in the industry for this task. We look at some of them and then propose a hybrid model.The methods we tried to implement are:• Slope-one Item-Item collaborative filtering• K-nearest neighbor user-user collaborative filtering• K-nearest neighbor item-item collaborative filtering• SVD• Incremental SVD• Incremental SVD with temporal dynamics• Content based recommendation• Demographic based recommendation
INTRODUCTION
ISOMAP of Movielens Data
DATA VISUALIZATION
Ø Slope one item-item collaborative filtering
• A regression model of a linear polynomial with slope 1, i.e only one independent variable which is trained. The model, though simple and computationally less intensive gives surprisingly good results.
Ø KNN item-item collaborative filtering
• Recommending movies based upon the similarity of rated items with k nearest neighbours in the dataset.• Similarity criteria – cosine, Euclidean distance, pearson correlation coefficient
Ø KNN user-user collaborative filtering
• Recommending movies based upon the similarity of users who rated an item with k nearest neighbours in the dataset.• Similarity criteria – cosine, Euclidean distance, pearson correlation coefficient• Generally, poorer result compared to KNN item-item CF
Ø Content based collaborative filtering
• Generates a feature for each item based upon the prior knowledge available for that item.• For movies – movie genre used to generate the feature vector.• Useful for users who have a sparse rating vector.
Ø Demographic based collaborative filtering
• Generates a feature for each user based upon the prior knowledge available for the user.• Age, gender and profession used to generate the feature vector
Ø SVD
• Projecting each user and item to a lower dimension (15 in our case).• Stochastic gradient descent to factorize the rating matrix to user and item feature matrix.• Learning rate 0.001, Num of iterations 200•
Ø Incremental SVD
• Similar to SVD except for including implicit feedback.• Reduced the data dimensionality to 5• Learning rate 0.004, Num of iterations 500
•
Ø Incremental SVD with temporal dynamics
• Similar to Incremental SVD except for time dependent user feature matrix.• All rating divided into 25 equally spaced time buckets• Learning rate 0.0005, Num of iterations 1100
•
METHODS
RMSE values for all methods on Movilens 100k dataset
RESULTS
CONCLUSION
• Combining KNN, Demographic, content-based and time-SVD++ methods using weighted mean, we achieve the RMSE value 0.91581557 i.e. a 1.5% improvement over the best individual method.
• Even a small improvement in RMS greatly impacts the top 10 suggestions given to the users[5]
• Isomap and locally linear embedding shows that the data has intrinsic lower dimensionality.
• Time-svd++ performed the best individually compared to all other methods.
REFRENCES
[1] Linden, Greg and Smith, Brent and York, Jeremy (2009) Amazon.comrecommendations: Item-to- item collaborative filtering[2] Robert M. Bell, Yehuda Koren, Chris Volinsky (2008) The BellKor 2008Solution to the Netflix Prize[3] Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor(2010) Recommender Systems Handbook[4] Yehuda Koren(2010) Collaborative filtering with temporal dynamics[5] Netflix Community- How useful is lower RMSEhttp://www.netflixprize.com/community/viewtopic.php?id=828
CONTACT
Ankush Sachdeva – 11120 – [email protected] Patel - 11362 – [email protected]
Khagesh Patel Ankush SachdevaMentored by Prof. Amitabh Mukerjee, Dept. of CSE, IIT Kanpur
Hybrid Recommendation System
Local Linear Embedding of Movielens Data
General rating behavior
Method RMSE
Slope one (item-item) 1.03136
KNN(user-user) 0.9439889
KNN(item-item) 0.9500658
Content based 1.8461
Demographic based 1.11833
SVD 0.942863
SVD++ 0.936
timeSVD++ 0.929762
Hybrid 0.915816