Deepak Jayaswal

As an essential task for recommender systems, the rating prediction problem over several contexts has attracted more attention over the recent years. The traditional approaches ignore the contexts and thus fail to predict the ratings for the unseen data in the rating tensor for varying contextual scenarios. Matrix factorization is preferred over decomposing the rating tensor for avoiding the burden of very high computational complexity while learning the interaction of users’ and items’ latent features. In this work, we propose a novel kernel loss function for optimizing the objective function of matrix factorization in a non-linearly projected rating space under multiple contexts in an optimum manner and also incorporate the implicit feedback of items in the learning process. Further, the optimization is regularized by applying different weights for each regularization term depending on the users’ and items’ participation. Extensive experimental evaluation on five benchmark context-aware datasets indicates the superiority of the proposed work for capturing the non-linearity and predicting the ratings of unseen items for users under varying contexts over the existing and baseline methods. The proposed kernel loss function is also shown to be resistant against shilling attacks in the recommender system. A detailed ablation study demonstrates the validity of the proposed work and the results are shown to be statistically significantly better with an improvement in the range of 3% to 11% over the baseline methods.

Due to the subjective nature of music mood, it is challenging to computationally model the affective content of music. In this work for estimating music mood, we present a deep Gaussian process regression model that is inspired from deep learning architectures. A variational Bayesian approach is used to learn the Gaussian mixture model representation for acoustic features. Since the exact inference on deep Gaussian processes (DGP) is intractable, the pseudo-data approximation is used to reduce the training complexity and Monte Carlo sampling technique is used to solve the intractability problem during training. A detailed derivation of a 3-layer DGP is presented that can be easily generalized to a L-layer DGP. The proposed work is evaluated on PMEmo dataset containing valence and arousal annotations of Western popular music and achieves an improvement in the coefficient of determination of 16.9% for arousal and 28.8% for valence estimation relative to the baseline single-layer Gaussian process.