Hybrid Collaborative Filtering with Autoencoders

Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. While Neu-ral Networks have tremendous success in image and speech recognition, they have received less attention in Collaborative Filtering. This is all the more surprising that Neural Networks are able to discover latent variables in large and heterogeneous datasets. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. We show experimentally on the MovieLens and Douban dataset that CFN outper-forms the state of the art and benefits from side information. We provide an implementation of the algorithm as a reusable plugin for Torch, a popular Neural Network framework

CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation

We proposes a novel deep neural network based recommendation model named Convolutional and Dense-layer Matrix Factorization (CDMF) for Context-aware recommendation, which is to combine multi-source information from item description and tag information. CDMF adopts a convolution neural network to extract hidden feature from item description as document and then fuses it with tag information via a full connection layer, thus generates a comprehensive feature vector. Based on the matrix factorization method, CDMF makes rating prediction based on the fused information of both users and items. Experiments on a real dataset show that the proposed deep learning model obviously outperforms the state-of-art recommendation methods

Computational Methods for Matrix/Tensor Factorization and Deep Learning Image Denoising

Feature learning is a technique to automatically extract features from raw data. It is widely used in areas such as computer vision, image processing, data mining and natural language processing. In this thesis, we are interested in the computational aspects of feature learning. We focus on rank matrix and tensor factorization and deep neural network models for image denoising. With respect to matrix and tensor factorization, we first present a technique to speed up alternating least squares (ALS) and gradient descent (GD) − two commonly used strategies for tensor factorization. We introduce an efficient, scalable and distributed algorithm that addresses the data explosion problem. Instead of a computationally challenging sub-step of ALS and GD, we implement the algorithm on parallel machines by using only two sparse matrix-vector products. Not only is the algorithm scalable but it is also on average 4 to 10 times faster than competing algorithms on various data sets. Next, we discuss our results of non-negative matrix factorization for hyperspectral image data in the presence of noise. We introduce a spectral total variation regularization and derive four variants of the alternating direction method of multiplier algorithm. While all four methods belong to the same family of algorithms, some perform better than others. Thus, we compare the algorithms using stimulated Raman spectroscopic image will be demonstrated. For deep neural network models, we focus on its application to image denoising. We first demonstrate how an optimal procedure leveraging deep neural networks and convex optimization can combine a given set of denoisers to produce an overall better result. The proposed framework estimates the mean squared error (MSE) of individual denoised outputs using a deep neural network; optimally combines the denoised outputs via convex optimization; and recovers lost details of the combined images using another deep neural network. The framework consistently improves denoising performance for both deterministic denoisers and neural network denoisers. Next, we apply the deep neural network to solve the image reconstruction issues of the Quanta Image Sensor (QIS), which is a single-photon image sensor that oversamples the light field to generate binary measures

Explaining Latent Factor Models for Recommendation with Influence Functions

Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is the lack of explainability. Extensive efforts have been made in the literature to incorporate explainability into LFMs. However, they either rely on auxiliary information which may not be available in practice, or fail to provide easy-to-understand explanations. In this paper, we propose a fast influence analysis method named FIA, which successfully enforces explicit neighbor-style explanations to LFMs with the technique of influence functions stemmed from robust statistics. We first describe how to employ influence functions to LFMs to deliver neighbor-style explanations. Then we develop a novel influence computation algorithm for matrix factorization with high efficiency. We further extend it to the more general neural collaborative filtering and introduce an approximation algorithm to accelerate influence analysis over neural network models. Experimental results on real datasets demonstrate the correctness, efficiency and usefulness of our proposed method