This repository contains the streaming component of the recommender engine. The component is written using Apache Spark.

The code in this repository has a simple structure. It does the following things:

• Fetches streaming user reviews from Kafka.
• Loads a learning model from a path specified in the arguments. (For the learning model, we use ALSModel.)
• Generates recommendations for users by using the learning model.
• Stores the generated recommendations in MongoDB.

This component cannot be run by itself. In order to run this component, all of the project components must be run using docker-compose. See Recommender Engine - Docker Files

If you want to change the code and run, after you change the code do the following steps:

• Go to the root directory of the repository.
• Run sbt clean assembly
• Get the recom-engine-docker repository from here.
• Move the jar file that is created from target/scala-2.11/recom-engine-streaming-assembly-0.1.jar to images/spark/master/target under recom-engine-docker's root directory.
• Go to the root diretory of recom-engine-docker and run the command docker-compose up.

If you want to watch the outputs of the streaming, do the following steps:

• First run all of the components with docker-compose.
• Go to the worker's web page from http://localhost:8082.
• In this page, you can see the running drivers, click stdout link of the streaming application, then you can watch the outputs of the application.

• Oğuzhan Bölükbaş
• Sercan Ersoy
• Yasin Uygun