Data 621 Blog Post 5 by Yohannes Deboch Focused on Recommender System by particular empasis on Netflix

I am inspired to use this post a blog poast from a team project that I was personally invoved . I felt like sharing at here as I belive it will add some value to the whole class. This blog post is aimed at practically showing how the recommender sytem works by partcularly analysing Netflix as a real worl scenario. Fisrt of all I would like to define what a recommender system means . We would like to start by defing what Recommnder Yystem means . A recommender system or a recommendation system (sometimes replacing "system" with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.Recommender systems are machine learning systems that help users discover new product and services. Every time you shop online, a recommendation system is guiding you towards the most likely product you might purchase.Recommender systems are an essential feature in our digital world, as users are often overwhelmed by choice and need help finding what they're looking for. This leads to happier customers and, of course, more sales. Recommender systems are like salesmen who know, based on your history and preferences, what you like.

Before going to sepacifically analyze how the netflix system works I would like to give some brief information on hoe the recommender sytem works in general .Relationships provide recommender systems with tremendous insight, as well as an understanding of customers. There are three main types that occur: User-Product Relationship The user-product relationship occurs when some users have an affinity or preference towards specific products that they need. For example, a cricket player might have a preference for cricket-related items, thus the e-commerce website will build a user-product relation of player->cricket. Product-Product Relationship Product-product relationships occur when items are similar in nature, either by appearance or description. Some examples include books or music of the same genre, dishes from the same cuisine, or news articles from a particular event. User-User Relationship User-user relationships occur when some customers have similar taste with respect to a particular product or service. Examples include mutual friends, similar backgrounds, similar age, etc. The Netflix business is a subscription service model that offers personalized recommendations, to help subscribers find shows and movies of interest. For this purpose they have created a proprietary, complex recommendations system.In most cases, these recommendations receives the highest hit which are then personalized to fit your preferences. They are driven by learning algorithms which is why each user's recommendations are different. Having a better set of recommendations makes subscribers to easly browse through the kind of content they like to watch.

When a user accesses the Netflix service, their recommendations system strives to help find a show or movie to enjoy with minimal effort. The system estimates the likelihood that the subscriber will watch a particular title in their catalog based on a number of factors including: the subscribers interactions with theor service (such as viewing history and how the customer rated other titles),other members with similar tastes and preferences on ther service , and information about the titles, such as their genre, categories, actors, release year, etc.

Besides knowing what the subscriber have watched on Netflix, to best personalize the recommendations they also look at things like: the time of day the subscriber watches, the devices the subscriber is watching Netflix on and the duration as well.

1. Who are your target customers?
Netflix is an internet streaming software which allows you to watch content through any internet connected device which include smart TVs, smartphones, tablets, game consoles etc. Netflix has 139 million subscribers globally. Target customers would be:

Anyone who is up to date with technology
People who do not have time to watch live tv
People who like to binge watch tv shows and movies
Households with kids

2. What are their key goals?
To get as much subscribers by improving the customer experience of their entertainment services. They try to get as much people to watch their content with unlimited hours.

3. How can you help them accomplish those goals?
This blog is amimed at highliting insights on how to improve the existing recommendation systetm as this is the best way to better understand their customers indirectly.

Netflix recommender system is based on collaborative filtering therefore they need a certain amount of data to understand it before making recommendations. When users sign up, the software usaually require the individual to choose a few shows to jump start their recommendations. They learn about the customer's preference based on:

How customers interact with the servie
Group customers in clusters of people with similar tastes and preferences
Information based on genre, actors, titles and the like

Also, with their ratings system, every time subscriber leaves a rating, they are learning what the subscriber enjoys.

Netflix recommendations are are deepely dependent on what you are currently watching hence you get stuck in a recommendation rut or lack variety of choices. However, the more you watch, the more your suggested content becomes relevant
Recommendations get diluted when multiple people are using the same profiles eventhough some times the app asks if it is being viewed by the same person or another when you re start the app
To show your approval, it is either a "thumbs up" or "thumbs down"

Based on the above brief analysis I belive the recomender system being utilized by netflix is satisfactory. The way they can improve their services is by doing an over all survey on the desires of their customers be creative through design , algorithm and exremnly brief inter-episode adds.

The following suggestion might be helpful

Due to the current trend on the anyone re-watching every movie or show that they previously watched is very slim will affect the efficiency of the sytem as it will lead the user's browsing experience less optimal, and as solution for this we recommend removing it or moving the list to the bottom would be good
Make it easy for people to share titles right in the platform or on social media. Offer a smarter way to dive in and explore content on your own
A good search engine with listed results would be ideal,with filters like "Added date," "Minimum user rating," "Director," or "Actor/Actress."

Explanatory Report on Recommender System: Its Practical Application on Netflix