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Item Based Collaborative Filtering Recommendation Algorithms

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Item Based Collaborative Filtering Recommendation Algorithms Badrul Sarvar, George Karypis, Joseph Konstan & John Riedl. http://citeseer.nj.nec.com/sarwar01itembased.html By Vered Kunik 025483819
Article Layout - ,[object Object],[object Object]
Article Layout – cont. ,[object Object],[object Object]
Introduction - ,[object Object],[object Object]
Introduction – cont. ,[object Object],[object Object]
Introduction – cont. ,[object Object],[object Object],[object Object]
Introduction – cont. ,[object Object]
The Collaborative Filtering Process - ,[object Object]
The CF Process – cont. ,[object Object],[object Object],[object Object],[object Object],[object Object]
The CF Process – cont. ,[object Object],[object Object],[object Object]
The CF Process – cont. ,[object Object]
Memory Based CF Algorithms - ,[object Object],[object Object]
Model Based CF Algorithms - ,[object Object],[object Object],[object Object]
Challenges Of User-based CF Algorithms - ,[object Object],[object Object],[object Object]
Challenges Of User-based CF Algorithms –cont. ,[object Object],[object Object],[object Object]
Item Based CF Algorithm - ,[object Object],[object Object]
Item Similarity Computation - ,[object Object],[object Object]
Item Similarity Computation – cont. ,[object Object],[object Object],[object Object]
Item Similarity Computation – cont. ,[object Object],[object Object],[object Object]
Item Similarity Computation – cont.
Prediction Computation - ,[object Object],[object Object]
Prediction Computation –cont. ,[object Object],[object Object],[object Object],[object Object]
Prediction Computation –cont. ,[object Object]
Performance Implications - ,[object Object],[object Object],[object Object],[object Object],[object Object]
Performance Implications - ,[object Object],[object Object],[object Object]
Experiments : The Data Set - ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments : The Data Set – cont. ,[object Object],[object Object]
Evaluation Metrics - ,[object Object],[object Object],[object Object]
Evaluation Metrics – cont. ,[object Object],The lower the MAE, the more accurately the recommendation engine predicts user ratings. MAE is the most commonly used and is the easiest to interpret.
Experimental Procedure - ,[object Object],[object Object],[object Object],[object Object]
Experimental Procedure – cont. ,[object Object],[object Object]
Experimental Results - ,[object Object],[object Object],[object Object],[object Object]
Effect of similarity Algorithms - ,[object Object]
Experimental Results – cont. ,[object Object],[object Object],[object Object],[object Object]
Sensitivity of Train/Test Ratio - ,[object Object]
Sensitivity of Train/Test Ratio - ,[object Object],[object Object],[object Object]
Experimental Results –cont. ,[object Object],[object Object],[object Object]
Experiments with neighborhood size - ,[object Object],[object Object],[object Object]
Quality Experiments - ,[object Object],[object Object]
Quality Experiments – cont. ,[object Object],[object Object]
Quality Experiments – cont. ,[object Object],[object Object],[object Object]
Scalability Challenges - ,[object Object],[object Object],[object Object],[object Object]
Scalability Challenges – cont. ,[object Object],[object Object],[object Object]
Scalability Challaenges – cont. ,[object Object]
Scalability Challenges – cont. ,[object Object],[object Object],[object Object]
Conclusions - ,[object Object],[object Object],[object Object],[object Object]
Conclusion – cont. ,[object Object],[object Object]
[object Object],[object Object]

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  • 1. Item Based Collaborative Filtering Recommendation Algorithms Badrul Sarvar, George Karypis, Joseph Konstan & John Riedl. http://citeseer.nj.nec.com/sarwar01itembased.html By Vered Kunik 025483819
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