Recommendations play a vital role in a great Netflix experience. Traditionally, these recommendations are precomputed using viewing history, scroll activity, and a variety of other signals in a near-line fashion. To be able to react more quickly to surges and dips in interest, we introduced the Trending Now row that makes use of real time data as an additional signal for generating recommendations. This allows us to not only personalize this row based on the context like time of day and day of week, but also react to sudden changes in collective interests of members, due to real-world events.
We will discuss the data pipeline that we built to process Netflix user activities in real time for the Trending Now row. We will share our experiences with developing, monitoring, and productionalizing a system that uses Kafka, Spark Streaming, and Cassandra.
Senior Software Engineer @Netflix
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