Collaborative Methods with Multiple Key Components and Domains for Recommender System

Graduate School of Economics and Management Collaborative Methods with Multiple Key Components and Domains for Recommender System by Nguyen Thi Thuy Linh Along with the convenience offered by increased use of the internet, people have gradually changed their habits. For instance, they shop online using e-commerce sites instead of going to stores. They watch movies on Netflix and YouTube as alternatives to going to a cinema. However, because information has propagated expeditiously, users have difficulty finding the items they want. Often, only a few items are visible to users while others are buried in a long-tailed list. For that reason, many recommender systems (RS) exist. My research addresses their problems and provides solutions based on deep learning models. The first challenge of an RS is suggesting interesting items to new users. To do so, an RS needs some interactions among users and items to occur. Hence, the system encounters serious obstacles with new or inactive users. To overcome this problem, modern RS tend to use as much information as possible. This trend was borne out of the increasing number of studies on hybrid methods that combine rating and auxiliary information. However, because of privacy concerns, in many cases, service providers can not require users to give their personal information. Therefore, numerous earlier reported methods only use item attributes for auxiliary information. To address these shortcomings, my manuscript provides a method to extract user profiles without using demographic data. My model learns user and item latent variables through two separate deep neural networks and also infers implicit relations between users and items using the information and their ratings. To deal with the lack of interactions among users and items and improve accuracy, RS tend to combine numerous kinds of information. Nevertheless, many useful data, such as item descriptions, items’ images or even transactions themselves, are unstructured, and traditional methods can not extract latent vectors effectively. Hence, how to obtain valuable information from unstructured data as well as how to integrate them into a single system has become the second challenge of RS. Recently, deep learning models have made a big step in extracting latent vectors of unstructured data and demonstrate their power in many applications from computer vision and natural language processing to RS and bioinformatics. My solutions are based on deep learning models to obtain better representation of user behavior and item description. The third challenge is that RS are mainly based on user interaction history, sometimes, suggestions only involve domains where the user interacted, which make user be tedious. To address this problem, I propose a cross-domain model that can suggest items in the other domains where the user even does not have any interaction. My domain-to-domain translation model (D2D-TM), which is based on generative adversarial network (GAN) and variational autoencoder (VAE), uses the user interaction history. Domain cycle consistency (CC) constrains the inter-domain relations.