Automated Recommender Systems

Recommender systems have been existing accompanying by web development, driving personalized experience for billions of users. They play a vital role in the information retrieval process, overcome the information overload by facilitating the communication between business people and the public, and boost the business world. Powered by the advances of machine learning techniques, modern recommender systems enable tremendous automation on the data preprocessing, information distillations, and contextual inferences. It allows us to mine patterns and relationships from massive datasets and various data resources to make inferences. Moreover, the fast evolvement of deep learning techniques brings vast vitality and improvements dived in both academic research and industry applications. Despite the prominence achieved in the recent recommender systems, the automation they have been achieved is still limited in a narrow scope. On the one hand, beyond the static setting, real-world recommendation tasks are often imbued with high-velocity streaming data. On the other hand, with the increasing complexity of model structure and system architecture, the handcrafted design and tuning process is becoming increasingly complicated and time-consuming. With these challenges in mind, this dissertation aims to enable advanced automation in recommender systems. In particular, we discuss how to update factorization-based recommendation models adaptively and how to automatically design and tune recommendation models with automated machine learning techniques. Four main contributions are made via tackling the challenges: (1) The first contribution of this research dissertation is the development of a tensor-based algorithm for streaming recommendation tasks. (2) As deep learning techniques have shown their superiority in recommendation tasks and become dominant in both academia and industry applications, the second contribution is exploring and developing advanced deep learning algorithms to tackle the recommendation problem with the streaming dataset. (3) To alleviate the burden of human efforts, we explore adopting automated machine learning in designing and tuning recommender systems. The third contribution of this dissertation is the development of a novel neural architecture search approaches for discovering useful features interactions and designing better models for the click-through rate prediction problem. (4) Considering a large number of recommendation tasks in industrial applications and their similarities, in the last piece of work work, we focus on the hyperparameter tuning problem in the transfer-learning setting and develop a transferable framework for meta-level tuning of machine learning models

SamBaTen: Sampling-based Batch Incremental Tensor Decomposition

Tensor decompositions are invaluable tools in analyzing multimodal datasets. In many real-world scenarios, such datasets are far from being static, to the contrary they tend to grow over time. For instance, in an online social network setting, as we observe new interactions over time, our dataset gets updated in its "time" mode. How can we maintain a valid and accurate tensor decomposition of such a dynamically evolving multimodal dataset, without having to re-compute the entire decomposition after every single update? In this paper we introduce SaMbaTen, a Sampling-based Batch Incremental Tensor Decomposition algorithm, which incrementally maintains the decomposition given new updates to the tensor dataset. SaMbaTen is able to scale to datasets that the state-of-the-art in incremental tensor decomposition is unable to operate on, due to its ability to effectively summarize the existing tensor and the incoming updates, and perform all computations in the reduced summary space. We extensively evaluate SaMbaTen using synthetic and real datasets. Indicatively, SaMbaTen achieves comparable accuracy to state-of-the-art incremental and non-incremental techniques, while being 25-30 times faster. Furthermore, SaMbaTen scales to very large sparse and dense dynamically evolving tensors of dimensions up to 100K x 100K x 100K where state-of-the-art incremental approaches were not able to operate

A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.Fil: Zhang, Jin. Nankai University; ChinaFil: Feng, Fan. Nankai University; ChinaFil: Han, TianYi. Nankai University; ChinaFil: Duan, Feng. Nankai University; ChinaFil: Sun, Zhe. Riken. Brain Science Institute; JapónFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Solé Casals, Jordi. Central University of Catalonia; Españ

Streaming data recovery via Bayesian tensor train decomposition

In this paper, we study a Bayesian tensor train (TT) decomposition method to recover streaming data by approximating the latent structure in high-order streaming data. Drawing on the streaming variational Bayes method, we introduce the TT format into Bayesian tensor decomposition methods for streaming data, and formulate posteriors of TT cores. Thanks to the Bayesian framework of the TT format, the proposed algorithm (SPTT) excels in recovering streaming data with high-order, incomplete, and noisy properties. The experiments in synthetic and real-world datasets show the accuracy of our method compared to state-of-the-art Bayesian tensor decomposition methods for streaming data