> In the broader sense, I guess my question is what are the next steps to build on this basic recommendation system? What is a good way to serve recommendations based on user behavior in near-real time and how do these systems take feedback to improve the models.
In the past, I have helped build Lambda architectures where we use a batch model to build a content vector space, build estimates of users in batch, update those in realtime (using PubSub/Kafka) based on user feedback.
Other online mechanisms could be to use Contextual Bandits: e.g. use context in terms of user interactions with the several arms of the bandits being recommendation choices etc. This interaction data can be used to continuously improve your policy. Of course, the key benefit over a Matrix Factorization setup where the interaction matrix is continuously rebuilt over time based on new data, is the in built exploration which minimizes regret.
In the past, I have helped build Lambda architectures where we use a batch model to build a content vector space, build estimates of users in batch, update those in realtime (using PubSub/Kafka) based on user feedback.
Other online mechanisms could be to use Contextual Bandits: e.g. use context in terms of user interactions with the several arms of the bandits being recommendation choices etc. This interaction data can be used to continuously improve your policy. Of course, the key benefit over a Matrix Factorization setup where the interaction matrix is continuously rebuilt over time based on new data, is the in built exploration which minimizes regret.