I guess I should elaborate a little more on what I am looking for. I already have a hybrid approach working where batch processing informs and improves the ALS models and these models are stored in memory to do some (Near)real time recommendations. The attractive bit about using something like Solr is that the user behavior on the app/front-end is easily modeled in terms or query parameters that could help serve better recommendations and also improve the model. It also seems to be the most commonly used approach based on what I have seen. I am wondering if there are other ways of doing this. 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.
eshvk|7 years ago
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.