Representation Learning for Cross-Domain Recommendation with Deep Neural Networks
The extraordinary amount of daily generated content by users represents an invaluable source of information to predict their intention, likes, and dislikes. This heterogeneous information comes in multiple forms, from simple preferences such as likes and ratings to texts, social interactions, videos, and images. Recommender systems are an effective solution to overcome information overload and have been indispensable in various information access systems as well facilitating decision-making processes. Usually, recommender systems focus on just one of these sources of information. While these diverse contents often contribute to complementary views on the user preferences, their combination under a unifying framework promises to provide a comprehensive and timely representation of the user. Deep learning architectures offer a versatile environment to combine such heterogeneous information in a principled way for predictive tasks which is essential as the recommendation performance depends on the ability to describe complex user-item relations and preference. This research project aims at defining a Deep Learning architecture for combining this variety of sources, transfer and adapt knowledge across multiple domains. The aspects to be looked into include long-term prediction, dynamic sequential decision-making, and resolving the dimensionality problem, particularly for complex systems, adaptation to new situations as well as the proposed model optimization for long term recommendation accuracy. A novel interactive Deep Reinforcement Learning (DRL) based recommendation system will be studied, as well as interactions between the system and environment simulated by Recurrent Neural Networks (RNN).