RESEARCH POSTER PRESENTATION DESIGN © 2012 Recommendation systems are widely used in e-commerce companies like Amazon, Net ix to help users discover items that they might not have found by themselves. 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 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. 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 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 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. 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 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 • Incremental SVD • Similar to SVD except for including implicit feedback. • Reduced the data dimensionality to 5 • Learning rate 0.004, Num of iterations 500 • 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.com recommendations: Item-to- item collaborative filtering [2] Robert M. Bell, Yehuda Koren, Chris Volinsky (2008) The BellKor 2008 Solution 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 RMSE http://www.netflixprize.com/community/viewtopic.php?id=828 CONTACT Ankush Sachdeva – 11120 – [email protected] Khagesh Patel - 11362 – [email protected] Khagesh Patel Ankush Sachdeva Mentored 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
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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
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