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Abstract
Cognitive services provide artificial intelligence (AI) technology for application developers, who are not required to be experts on machine learning. Cognitive services are presented as an integrated service platform where end users bring abilities such as seeing, hearing, speaking, searching, user profiling, etc. to their own applications under development via simple API calls. As one of the above abilities, recommender systems serve as an indispensable building brick, especially when it comes to the information retrieval functionality in the cognitive service platform. This thesis focuses on the novel recommendation algorithms that are able to improve on recommendation quality measured by accuracy metrics, e.g., precision and recall, with advanced deep learning techniques. Recent deep learning-based recommendation models have been proved to have state-ofthe-art recommendation quality in a host of recommendation scenarios, such as rating prediction tasks, top-N ranking tasks, sequential recommendation, etc. Many of them only leverage the existing information acquired from users’ past behaviours to model them and make one or a set of predictions on the users’ next choice. Such information is normally sparse so that an accurate user behaviour model is often difficult to obtain even with deep learning. To overcome this issue, we invent various adversarial techniques and apply them to deep learning recommendation models in different scenarios. Some of these techniques involve generative models to address data sparsity and some improve user behaviour modelling by introducing an adversarial opponent in model training. We empirically show the effectiveness of our novel techniques and the enhancement achieved over existing models via thorough experiments and ablation studies on widely adopted recommendation datasets.
The contributions in this thesis are as follows:
1. Propose the adversarial collaborative auto-encoder model for top-N recommendation;
2. Propose a novel deep domain adaptation cross-domain recommendation model for rating prediction tasks via transfer learning;
3. Propose a novel adversarial noise layer for convolutional neural networks and a convolutional generative adversarial model for top-N recommendation.
