Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of user-item interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines.Comment: 8 pages, 5 figures, ICPR2020 conferenc

Bayesian latent variable models for collaborative item rating prediction

Collaborative filtering systems based on ratings make it easier for users to find content of interest on the Web and as such they constitute an area of much research. In this paper we first present a Bayesian latent variable model for rating prediction that models ratings over each user's latent interests and also each item's latent topics. We describe a Gibbs sampling procedure that can be used to estimate its parameters and show by experiment that it is competitive with the gradient descent SVD methods commonly used in state-of-the-art systems. We then proceed to make an important and novel extension to this model, enhancing it with user-dependent and item-dependant biases to significantly improve rating estimation. We show by experiment on a large set of real ratings data that these models are able to outperform 3 common baselines, including a very competitive and modern SVD-based model. Furthermore we illustrate other advantages of our approach beyond simply its ability to provide more accurate ratings and show that it is able to perform better on the common and important case where the user profile is short

Recovery of Missing Values using Matrix Decomposition Techniques

Time series data is prominent in many real world applications, e.g., hydrology or finance stock market. In many of these applications, time series data is missing in blocks, i.e., multiple consecutive values are missing. For example, in the hydrology field around 20% of the data is missing in blocks. However, many time series analysis tasks, such as prediction, require the existence of complete data. The recovery of blocks of missing values in time series is challenging if the missing block is a peak or a valley. The problem is more challenging in real world time series because of the irregularity in the data. The state-of-the-art recovery techniques are suitable either for the recovery of single missing values or for the recovery of blocks of missing values in regular time series. The goal of this thesis is to propose an accurate recovery of blocks of missing values in irregular time series. The recovery solution we propose is based on matrix decomposition techniques. The main idea of the recovery is to represent correlated time series as columns of an input matrix where missing values have been initialized and iteratively apply matrix decomposition technique to refine the initialized missing values. A key property of our recovery solution is that it learns the shape, the width and the amplitude of the missing blocks from the history of the time series that contains the missing blocks and the history of its correlated time series. Our experiments on real world hydrological time series show that our approach outperforms the state-of-the-art recovery techniques for the recovery of missing blocks in irregular time series. The recovery solution is implemented as a graphical tool that displays, browses and accurately recovers missing blocks in irregular time series. The proposed approach supports learning from highly and lowly correlated time series. This is important since lowly correlated time series, e.g., shifted time series, that exhibit shape and/or trend similarities are beneficial for the recovery process. We reduce the space complexity of the proposed solution from quadratic to linear. This allows to use time series with long histories without prior segmentation. We prove the scalability and the correctness of the solution

Goal-driven Collaborative Filtering

Recommender systems aim to identify interesting items (e.g. movies, books, websites) for a given user, based on their previously expressed preferences. As recommender systems grow in popularity, a notable divergence emerges between research practices and the reality of deployed systems: when recommendation algorithms are designed, they are evaluated in a relatively static context, mainly concerned about a predefined error measure. This approach disregards the fact that a recommender system exists in an environment where there are a number of factors that the system needs to satisfy, some of these factors are dynamic and can only be tackled over time. Thus, this thesis intends to study recommender systems from a goal-oriented point of view, where we define the recommendation goals, their associated measures and build the system accordingly. We first start with the argument that a single fixed measure, which is used to evaluate the system’s performance, might not be able to capture the multidimensional quality of a recommender system. Different contexts require different performance measures. We propose a unified error minimisation framework that flexibly covers various (directional) risk preferences. We then extend this by simultaneously optimising multiple goals, i.e., not only considering the predicted preference scores (e.g. ratings) but also dealing with additional operational or resource related requirements such as the availability, profitability or usefulness of a recommended item. We demonstrate multiple objectives through another example where a number of requirements, namely, diversity, novelty and serendipity are optimised simultaneously. At the end of the thesis, we deal with time-dependent goals. To achieve complex goals such as keeping the recommender model up-to-date over time, we consider a number of external requirements. Generally, these requirements arise from the physical nature of the system, such as available computational resources or available storage space. Modelling such a system over time requires describing the system dynamics as a combination of the underlying recommender model and its users’ behaviour. We propose to solve this problem by applying the principles of Modern Control Theory to construct and maintain a stable and robust recommender system for dynamically evolving environments. The conducted experiments on real datasets demonstrate that all the proposed approaches are able to cope with multiple objectives in various settings. These approaches offer solutions to a variety of scenarios that recommender systems might face