An Efficient Collaborative Recommender System based on k -separability Georgios Alexandridis Georgios Siolas Andreas Stafylopatis Department of Electrical and Computer Engineering National Technical University of Athens 20 th International Conference on Artificial Neural Networks (ICANN 2010) Alexandridis, Siolas, Stafylopatis (NTUA) k -separability Collaborative Recommender ICANN’10 1 / 16
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An Efficient Collaborative Recommender Systembased on k -separability
Georgios Alexandridis Georgios Siolas Andreas Stafylopatis
Department of Electrical and Computer EngineeringNational Technical University of Athens
20th International Conference on Artificial Neural Networks(ICANN 2010)
Recommender Systems attempt to present information items (e.g.movies, music, books, news stories) that are likely to be of interestto the user.Some implementations
I AmazonF "Customers Who Bought This Item Also Bought"
The added noise in the dataset hinders the discovery of patternsin data
I Data clusters become difficult to separate
Machine Learning techniques for highly non-separable datasets
I Support Vector Machines, Radial Basis Functions
F Computing the support vector (or estimating the surface . . . ) can be acomputationally intensive task
I Evolutionary Algorithms
F Meaningful Recommendations are not always guaranteed(evolutionary dead-ends)
I Our approach: Use k -separability!
F Originally proposed by W. Duch1
F Special case of the more general method of Projection PursuitF Application to Feed-Forward ANNsF Extends linear separability of data clusters into k > 2 segments on
The added noise in the dataset hinders the discovery of patternsin data
I Data clusters become difficult to separateMachine Learning techniques for highly non-separable datasets
I Support Vector Machines, Radial Basis Functions
F Computing the support vector (or estimating the surface . . . ) can be acomputationally intensive task
I Evolutionary Algorithms
F Meaningful Recommendations are not always guaranteed(evolutionary dead-ends)
I Our approach: Use k -separability!
F Originally proposed by W. Duch1
F Special case of the more general method of Projection PursuitF Application to Feed-Forward ANNsF Extends linear separability of data clusters into k > 2 segments on
F Meaningful Recommendations are not always guaranteed(evolutionary dead-ends)
I Our approach: Use k -separability!
F Originally proposed by W. Duch1
F Special case of the more general method of Projection PursuitF Application to Feed-Forward ANNsF Extends linear separability of data clusters into k > 2 segments on
F Meaningful Recommendations are not always guaranteed(evolutionary dead-ends)
I Our approach: Use k -separability!F Originally proposed by W. Duch1
F Special case of the more general method of Projection PursuitF Application to Feed-Forward ANNsF Extends linear separability of data clusters into k > 2 segments on
Extending linear separability to 3-separabilityThe 2-bit XOR problem
A highly non-separable datasetIt can be learned by a 2-layered perceptron, or ......by a single layer percpetron that implements k -separability!The activation function must partition the input space into 3distinct areas
Extending linear separability to 3-separabilityThe 2-bit XOR problem
A highly non-separable datasetIt can be learned by a 2-layered perceptron, or ......by a single layer percpetron that implements k -separability!The activation function must partition the input space into 3distinct areas
The index of separability (k ) is not known a-prioriI Setting k to a fixed value is of little helpI It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimatedDynamic Network ArchitectureSparse user ratings’ matrix ⇒ small overall network size ⇒Constructive Network AlgorithmOur constructive network algorithm was derived from the NewConstructive Algorithm2
2Islam MM et al. A new constructive algorithm for architectural and functional adaptation of artificial neural
networks.
IEEE Trans Syst Man Cybern B Cybern. 2009 Dec;39(6):1590-605
The index of separability (k ) is not known a-prioriI Setting k to a fixed value is of little helpI It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimated
Dynamic Network ArchitectureSparse user ratings’ matrix ⇒ small overall network size ⇒Constructive Network AlgorithmOur constructive network algorithm was derived from the NewConstructive Algorithm2
2Islam MM et al. A new constructive algorithm for architectural and functional adaptation of artificial neural
networks.
IEEE Trans Syst Man Cybern B Cybern. 2009 Dec;39(6):1590-605
The index of separability (k ) is not known a-prioriI Setting k to a fixed value is of little helpI It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimatedDynamic Network Architecture
Sparse user ratings’ matrix ⇒ small overall network size ⇒Constructive Network AlgorithmOur constructive network algorithm was derived from the NewConstructive Algorithm2
2Islam MM et al. A new constructive algorithm for architectural and functional adaptation of artificial neural
networks.
IEEE Trans Syst Man Cybern B Cybern. 2009 Dec;39(6):1590-605
The index of separability (k ) is not known a-prioriI Setting k to a fixed value is of little helpI It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimatedDynamic Network ArchitectureSparse user ratings’ matrix ⇒ small overall network size ⇒Constructive Network Algorithm
Our constructive network algorithm was derived from the NewConstructive Algorithm2
2Islam MM et al. A new constructive algorithm for architectural and functional adaptation of artificial neural
networks.
IEEE Trans Syst Man Cybern B Cybern. 2009 Dec;39(6):1590-605
The index of separability (k ) is not known a-prioriI Setting k to a fixed value is of little helpI It can lead to either overspecialization or to large training errors
Therefore, k is a problem parameter: it has to be estimatedDynamic Network ArchitectureSparse user ratings’ matrix ⇒ small overall network size ⇒Constructive Network AlgorithmOur constructive network algorithm was derived from the NewConstructive Algorithm2
2Islam MM et al. A new constructive algorithm for architectural and functional adaptation of artificial neural
networks.
IEEE Trans Syst Man Cybern B Cybern. 2009 Dec;39(6):1590-605
Contains the ratings of 943 users on1682 moviesSparse matrix (6.3% of non-zeroelements)Each user has rated at least 20movies (106 on average), but. . .Discrete Exponential Distribution
I 60% of all users have rated 100movies or less
I 40% of all users have rated 50movies or less
We followed a purely CollaborativeStrategy
I Taking into account only the userratings’ and not any otherdemographic information
We have presented a complete Collaborative RecommenderSystem that is specifically fit for those cases where information islimitedOur system achieves a good trade-off between Precision andRecall, a basic requirement for RecommendersThis is due to the fact that k -separability is able to uncovercomplex statistical dependencies (positive and negative)We don’t need to filter the neighborhood of the target user as othersystems do (e.g. by using the Pearson Correlation Formula).
I All "neighbors" are consideredI Extremely useful in cases of sparse datasets