In this diagram, we break down the system architecture of the Netflix recommendation system, analyzing the technical component of our socio-technical system on the recommendation engine.”We identified four different layers in the architecture of the Netflix recommendation engine: user interaction, function, algorithm, and database. Furthermore, we identify the elements that the user interaction, function, and algorithms and database shape.
In the user interaction layer of the Netflix recommendation engine architecture, there is a variety of elements involved within this sector: the search feature, the feedback users share, featured rows such as the “genre”, “top pick”, and “because you watched” row. These user interaction features do a variety of different things. The feedback feature allows for users to voice out their opinion on whether or not they feel that the recommendation given to them are useful or not. The “genre” row is tailored by personalization of the preferred genre as identified by the user. The “top pick row” is curated based off of popularity and a user’s identified viewing trends. The “because you watched” row is a series of recommendations based of a user’s viewing history. The Netflix recommendation engine is built on a hybrid filter.
Within the algorithm, a user’s search functions in a capacity to give the user a result for the search, predict a user’s request from a partial input, and find videos to recommend as options for the user’s search. With users giving feedback, it changes the overall result selections. The “genre” rows personalizes top picks using the user’s genre preferences. The impact the algorithm has on the “top pick row” is that it uses data from user viewing patterns to determine rankings on what the top videos are based on popularity of the users. The algorithm functions in the “because you watched” row by organizing videos based on its similarity.
In conclusion all of the factors of the user interaction, functionality, and algorithm, go hand in hand and flow together in order to improve and correct recommendations to the user’s taste preferences.
