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Found 9 papers, 1 papers with code
GPU accelerated matrix factorization of large scale data using block based approach
Matrix Factorization (MF) on large scale data takes substantial time on a Central Processing Unit (CPU).
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data
In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique.
Decomposing the Deep: Finding Class Specific Filters in Deep CNNs
Interpretability of Deep Neural Networks has become a major area of exploration.
Inductive Conformal Recommender System
The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value.
A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data
By making use of hinge loss function we transfer the learnt codebook of the source domain to target.
Block based Singular Value Decomposition approach to matrix factorization for recommender systems
With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems.
Committee Selection with Attribute Level Preferences
We describe a greedy approach for attribute aggregation that satisfies the first three properties, but not the fourth, i. e., compound justified representation, which we prove to be NP-complete.
Group Preserving Label Embedding for Multi-Label Classification
Multi-label learning is concerned with the classification of data with multiple class labels.
Multi-cell LSTM Based Neural Language Model
Long Short-Term Memory (LSTM) architecture solves the inadequacies of the standard RNN in modeling long-range contexts.
