On deep multi-view representation learning.Wang, W., Arora, R., Livescu, K. and 1 more (2015) 32nd International Conference on Machine Learning, ICML 2015, 2, pp. 1083-1092.
Recent online services rely heavily on automatic personal-ization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommo-date the stream of new users visiting the online services for the first time. In this work, we propose a content-based rec-ommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web brows-ing history and search queries. We use a Deep Learning ap-proach to map users and items to a latent space where the similarity between users and their preferred items is maxi-mized. We extend the model to jointly learn from features of items from different domains and user features by intro-ducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any in-teraction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommen-dation, News recommendation, and Movie/TV recommen-dation. Results indicate that our approach is significantly better than the state-of-The-Art algorithms (up to 49% en-hancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in compar-ison with transitional generative topic models, for model-ing cross-domain recommender systems. Scalability analy-sis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.