Large Scale Tensor Regression using Kernels and Variational Inference

We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against \textit{LightGBM} and \textit{Field Aware Factorization Machines}(FFM), two algorithms with proven track records widely used in industrial forecasting. We also develop a variational inference framework for KFT and associate our forecasts with calibrated uncertainty estimates on three large scale datasets. Furthermore, KFT is empirically shown to be robust against uninformative side information in terms of constants and Gaussian noise

Modelling and analysis of temporal preference drifts using a component-based factorised latent approach

In recommender systems, human preferences are identified by a number of individual components with complicated interactions and properties. Recently, the dynamicity of preferences has been the focus of several studies. The changes in user preferences can originate from substantial reasons, like personality shift, or transient and circumstantial ones, like seasonal changes in item popularities. Disregarding these temporal drifts in modelling user preferences can result in unhelpful recommendations. Moreover, different temporal patterns can be associated with various preference domains, and preference components and their combinations. These components comprise preferences over features, preferences over feature values, conditional dependencies between features, socially-influenced preferences, and bias. For example, in the movies domain, the user can change his rating behaviour (bias shift), her preference for genre over language (feature preference shift), or start favouring drama over comedy (feature value preference shift). In this paper, we first propose a novel latent factor model to capture the domain-dependent component-specific temporal patterns in preferences. The component-based approach followed in modelling the aspects of preferences and their temporal effects enables us to arbitrarily switch components on and off. We evaluate the proposed method on three popular recommendation datasets and show that it significantly outperforms the most accurate state-of-the-art static models. The experiments also demonstrate the greater robustness and stability of the proposed dynamic model in comparison with the most successful models to date. We also analyse the temporal behaviour of different preference components and their combinations and show that the dynamic behaviour of preference components is highly dependent on the preference dataset and domain. Therefore, the results also highlight the importance of modelling temporal effects but also underline the advantages of a component-based architecture that is better suited to capture domain-specific balances in the contributions of the aspects

Temporal Dynamics in Recommender Systems

In real-world scenarios, user preferences for items are constantly drifting over time as item perception and popularity are changing when new fashions or products emerge. The ability to model the tendency of both user preferences and item attractiveness is thus vital to the design of recommender systems (RSs). However, conventional methods in RSs are incapable of modeling such a tendency accordingly, leading to an unsatisfactory recommendation performance. This thesis proposes a framework for the temporal dynamics problem in RSs. The temporal properties and dynamic information in user preferences and item attractiveness derived from user feedback over items are modeled, learned and applied to predict user preferences on items over time. The framework provides original solutions to improve the performance of RSs by incorporating and exploiting this significant but traditionally neglected information. Firstly, a novel probabilistic temporal model for RSs is developed to tackle the inherent nonlinear and non-Gaussian dynamic problem with the complex and diverse real-world recommendation scenarios. It tracks simultaneously latent factors that represent user preferences and item attractiveness. A learning and inference algorithm combining a sequential Monte Carlo method and the Expectation-Maximization algorithm for this model is developed to tackle the top-k recommendation problem over time. Secondly, a novel probabilistic personalized and item-wise temporal model is proposed to solve the cold start transition (CST) problem by collaborative tendencies without any prior assumptions about the structure of the dynamics. The CST problem is first defined in this thesis, which is a result that users often leave feedback on an item only once and on only one period, preventing from learning any dynamics directly. Finally, a Bayesian Wishart matrix factorization method is proposed to model the temporal dynamics of variances due to sudden changes and other local temporal effects among user preferences and item attractiveness. It combines the collapsed Gibbs sampling method and the elliptical slice sampling method.The presented models and learning algorithms are validated experimentally on several real-world public benchmark datasets. The experimental results demonstrate that those models and algorithms significantly outperform a variety of state-of-art methods in RSs