Collecting and utilizing user data is essential for effective recommender systems to personalize content. However, privacy and compliance regulations protect personal user data. With strict regulations such as the General Data Protection Regulation (GDPR) or California Privacy Rights Act (CPRA) in effect, one may ask: how can a recommender system be both compliant and effective? This paper aims to answer this question, demonstrating privacy-compliant personalization for the Recommended Documents service within Microsoft 365 (M365), particularly Microsoft Feed. It outlines the development of an exemplary L-Profile personalization feature from conception to productionization, covering offline and online evaluations.

Email responses often contain items---such as a file or a hyperlink to an external document---that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.

In recent world, we have been surrounded by various social networks (SNs). In every company, every institution and every place worldwide, people call each other, exchange emails, text messages, post in forums, co-author documents, meet at diverse events, etc. In other words, they communicate and collaborate with each other creating and maintaining mutual relationships in a complex SN. The traditional expert finding systems try to locate a relevant expert to whom the query should be sent. However, most of the experts are not willing to solve the problems for people they do not know. In our proposed novel system, a social paradigm is used to encourage experts to send their solutions. By means of recommendations, the system propagates the query not directly to the expert, but to friends and colleagues of the expert through the acquaintance chain existing in the SN. The experts are more likely to answer if the requests come from their acquaintances. The general idea, model and simulations on the recommender system for query propagation in the SN are presented in the paper.