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

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting). This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning. Our key intuition is to guide neural collaborative filtering with domain-invariant components shared across the dense and sparse domains, improving the user and item representations learned in the sparse domains. We leverage contextual invariances across domains to develop these shared modules, and demonstrate that with user-item interaction context, we can learn-to-learn informative representation spaces even with sparse interaction data. We show the effectiveness and scalability of our approach on two public datasets and a massive transaction dataset from Visa, a global payments technology company (19% Item Recall, 3x faster vs. training separate models for each domain). Our approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202

Outfit Recommender System

The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommendation systems on retail websites generate a lot of this revenue. Thus, improving recommendation systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommendation system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user