Have you ever chosen a product at a local store, based on the recommendation from the owner or sales clerk? I am sure you have done that. Similar recommendations are made by e-commerce websites. But, how does a website know what ‘Products you may like?’ This is where the recommendation system or a recommendation algorithm has its role.
What is a Recommendation System?
A recommendation system is a machine-learning algorithm that suggests products, services, events, etc. to users based on analysis of data. It basically acts as a filter, which helps users to discover new products and services they would like. Every time that you use an online service, or look for a product, a recommendation system is guiding you towards the product you are most likely to purchase. These system algorithms are like salesmen, who know what you would be interested in, based on your history and preferences.
It is imperative to have these system algorithms, since most of the users are overwhelmed by the range of e-commerce services, and need reliable help to find what they’re looking for. This leads to happy customers, and obviously, more sales.
Some examples of recommender systems in action include product recommendations on Amazon and Flipkart, suggestions for movies and TV shows by Netflix, recommended videos on YouTube, music on Spotify, MakeMyTrip for recommending rooms and hotels, and Google pop-up Ads.
Relationships provide recommendation algorithm with tremendous insights, as well as an understanding of customers. Utilizing machine learning techniques and various data about both individual products and individual users, there are three main types of relationships that occur within system algorithm:
1. User-Product relationships
These are based on users’ individual product preferences. It occurs when some users have affinity or preference towards specific products that they need or buy frequently. For e.g., an avid reader would have an affinity towards different genres of books, bookmarks, book holders, etc. So, a product -> user connection is created here and recommendations would be based on it.
2. Product-Product Relationship
Product-product relationship occurs when items are similar in nature, either by their appearance or description. For e.g., books and movies of the same genre. The products may also be complementary in nature, like printer, ink and cartridge; cricket bat, ball, and gloves. ‘Frequently bought together’ recommendations are usually based on this relationship.
3. User-User Relationship
These relationships are based on similar interests of customers with respect to a particular product or service. For instance, mutual friends, or people with similar backgrounds, similar age, etc. might be interested in similar kinds of products. An easy example is, if a person who is 16 years old, and is interested in chemistry books, chances are you might need it too.
Approach For Filtering Data - Collaborative Vs Content
The purpose of a recommendation algorithm system is to suggest relevant products or services to users. To achieve this task, there exist two major categories of methods: Collaborative Filtering and Content-Based Filtering methods.
The Collaborative Filtering Method
The Collaborative Filtering Method technique recommends products based on the past interactions between users and products. When you are looking for a product, the recommendation algorithm incorporates data from users who have purchased similar products, then combines that information to suggest similar recommendations to you as well.
Here’s an example: If customer A likes rom com books that customer B also likes, then assuming that they have similar interests, collaborative-filtering would recommend other products purchased by customer B to customer A, and vice versa.
The Content-Based Filtering Method
The Content-Based Filtering method makes recommendations mainly by relying on cookies (data stored on the user’s computer by the web browser while browsing a website). The algorithm analyses customer data on the likes and dislikes of each user. The idea is that if you enjoy a certain service, you might also enjoy similar services. An example is that, if you’re watching a murder mystery movie, you’ll probably enjoy other mystery movies also. If you are constantly listening to jazz music, the filtering system would take that information and begin recommending similar music to you based on the songs you preferred.
A hybrid method combines the content-based and collaborative-based methods together to incorporate decisions and make recommendations. But the key focus, and the output recommendation is based on attributes of a specific user.
Netflix is a good example of the use of hybrid recommender systems, wherein recommendations are made by comparing the watching and searching habits of similar users (collaborative filtering) as well as by offering movies that share characteristics with films that a user has watched or liked (content-based filtering).
All the three methods are based on machine-learning algorithms and provide personalized product recommendations.
Review Based Recommender Systems
In popular e-commerce sites and social media, users frequently provide reviews, giving personal opinions about a wide array of products and services they’ve used. These reviews which may be in the form of text, or star ratings , serve as a rich source of information about the users’ preferences. In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated reviews into the recommending process. Advanced text mining and sentiment analysis techniques enable the extraction of various types of review elements, comparative opinions, and reviewers’ emotions. Eventually, the highest-rated products are recommended to users based on their preferences.
Future Recommendation Systems
So, this is the algorithm which is used to recommend products that we may like. We’re usually amazed by the intelligence of websites which recommend great products to us. But there’s a lot involved behind it all, which was briefly explained here.
The future of recommender systems lie in integrating self actualization to do justice to serendipity while recommending services and understanding preferences. Here we are trying to achieve a recommendation which is not extremely personalised , and may feel intrusive to the user. Striking a balance between the two is what needs to be achieved.
A wide range of hybrid systems will be emerging in future which combine various permutations and combinations of the techniques mentioned above. Furthermore, a lot of work is needed to be done on the cold start problem- which will perform automated data modelling, in order to somehow manage collecting just the right amount of implicit information and data to recommend users even if there is little or no direct information available on them.