Recommendation Engines: A Game changer for Online Retailers

Content-based Recommendation

A Content-Based Recommender works by assigning certain attributes to the user and the content they sell and then finds the best match for each user. It assigns attributes by collecting data from the user, either explicitly (by asking the preferences of the user) or implicitly (information the user himself provides). The recommender becomes more accurate with each interaction of the user on the platform as the system keeps collecting data and running it through the attributes.

This type of recommendation system relies on the attributes of the products themselves. It doesn’t require its users to interact before making recommendations which is the cold start problem.

if a user likes a song the system will try to recommend songs matching the attributes of that song. Attributes could be the singer, category, genre, language, etc. This is how content-based filtering works.

Collaborative Filtering Recommendation

Collaborative filtering works on how other users rated in the past and not based on the attributes of the product. It finds similarities in users preferences, and classifies them by their choices. Then it gives recommendations based on the best matches. The underlying concept is that people who have similar interests could be clustered in groups and any new user who shows interest matching a particular group would like the items group’s members like. Collaborative filtering relies predominantly on other users’ past data to show recommendations.

For example, if two users watch the same set of movies , their choices in movies match. And we can also say that it is highly probable those movies have similar attributes. To understand better, consider a person X who watches movies A,B,C,D & E and another person Y who watches movies B,C,D,E & F. Both X and Y have watched B,C,D & E, so the recommender would consider their taste similar and suggest movie F to person X (Who has watched all other movies watched by Y except movie F) and would suggest movie A to person Y (Who has watched all other movies watched by X except movie A).  This is what helps in making recommendations to users.

Currently, music streaming giant Spotify has 286 million active users, 50 million tracks and over 4 billion playlists[1]. Why Spotify is a favorite online music streaming platform globally is its algorithm, what sets it apart from competitors. Spotify relies on its algorithm to recommend some of the best playlists to the users. One of its popular “discover weekly” playlist is a hit worldwide. Its algorithm gathers data about the music history and also analyses the pattern of people listening to a particular song, be it a cultural pattern, age, gender, time or the geography. 

An AI system called ‘Bandits for Recommendations as Treatments’ (simply known as BaRT)[2] is the real reason behind Spotify’s awesome recommendations. Let’s hear from the brilliant people who know it from inside out:

Tony Jebara, Who was brought in from Netflix, VP of Engineering and Head of Machine Learning at Spotify explained about its algorithm at TensorFlow World in Santa Clara, California[3]. He talked about how the algorithm works on exploitation and exploration techniques to provide the best recommendations to its users. Exploitation is when recommendations are based on what the user has listened to in the past. Exploration is based on user interaction with the platform and is used as a  research tool to draw patterns among users behaviour.

How Does Tinder Decide Who You’ll See Next?

Tinder algorithm works in a way that it uses user data to create an “ELO” score(the same system is used to calculate the relative skill levels of players in zero-sum games such as chess. The name comes from its creator Arpad Elo) which is a measurement of the quality and quantity of your matches. Your rank gets better depending on numbers of right swipes you get, and also who are swiping(basically their ranks). Tinder would then recommend people with the same score. It assumes that people with similar opinions would be in approximately the same tier compatibility.

Have You Ever Thought of Google as a Recommendation Engine?

Have you ever noticed how Google completes your sentences, how many times you wouldn’t have to even type more than two words and Google knows what you want. Google uses machine learning, predictive analytics, natural language processing to show relevant choices specific for each user. How do they do that? Well, nobody knows what is the exact algorithm but it depends on query volume, geography, demographics and keyword popularity. But there’s a lot more to it that experts keep guessing for us.

Google uses recommendations not only for search queries but also during ads. You must have noticed that Google ads are very relevant. They are based on your interest level for a particular item at that point. Most of the time, the ads would be of last item you checked on any shopping website. Google charges a hefty amount to these shopping websites for such ads. Everyone would have at least used one of the many services Google provides. Google has many opensource platforms like play store, android, search engine or its devices. By using these popular services, you agree to share a lot about you with Google. It knows a lot about you, it knows what you do, what you speak, where you live & where you study/work because we search so much that even this process of searching is called Googling. And that’s how it shows you the most relevant things by feeding your information.