Renaud Bourassa
ABSTRACT
Every day, millions of people rely on Slack to get the information they need to do their jobs. To make their working lives more productive, Slack has built a number of recommender systems to prioritize the content a given user is most likely to need at any point in time. These systems have wide-ranging purposes, from recommending channels for users to join to ranking unread content so users can catch up more easily.
A common trait of all these systems is that they must deal with strict privacy boundaries inherent to the underlying dataset. By policy, users can only be exposed to data that was publicly shared in their own Slack team. These restrictions must carry over into the recommender systems: not only must they refrain from recommending data from foreign teams, but ---more subtly--- patterns in foreign teams' data must not be inferable from the usage of these systems.
In this talk, I will discuss how Slack's dataset differs from those used in traditional recommender systems such as the Netflix Prize dataset. I will also present some techniques we developed to leverage the entire dataset to improve the performance of our recommender systems without jeopardizing the privacy boundaries we guarantee to our customers. These include a mix of algorithms with increased locality as well as the use of metadata over data to generate privacy sensitive recommendations.
Supplemental Material
Index Terms
- Building recommender systems with strict privacy boundaries
Recommendations
Building recommender systems for scholarly information
SWM '17: Proceedings of the 1st Workshop on Scholarly Web MiningThe depth and breadth of research now being published is overwhelming for an individual researcher to keep track of let alone consume. Recommender systems have been developed to make it easier for researchers to discover relevant content. However, these ...
A unified approach to building hybrid recommender systems
RecSys '09: Proceedings of the third ACM conference on Recommender systemsContent-based recommendation systems can provide recommendations for "cold-start" items for which little or no training data is available, but typically have lower accuracy than collaborative filtering systems. Conversely, collaborative filtering ...
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
Comments