The Recommendation System
Feb. 7, 2024, 4:04 p.m.
Since the last decades, with the faster and cheaper internet services, disruptive personalized mobile computing there has been exhaustive amount of online information creation and consumption. There has been significant rise of online service provider and content creator websites like YouTube, NetFlix, Amazon, Facebook, Instagram etc. which is directly or indirectly each and every aspect of our daily life. Since today’s world is overwhelmed with vast source of information, there is huge demand of recommendation system technologies, that can help the users to choose from vast set of options available.
Thus, recommendation system can be defined as information filtering algorithm that aim to provide the most relevant information to the user from the vast pool of information. These systems discover pattern in the dataset by learning user’s behaviour and produces outcomes that correlates users interests and preferences.Example of recommendation system can be E-commerce website like Amazon recommending its customer the product of their choice, or YouTube recommending its visitor the videos they would like to watch.
Recommendation system can generate huge amount of income for the business and can help the company to gain competitive advantages over its peers.Now a days it has become so critical for any online business that Netflix had organized competition worth $1 Million to design recommendation system for them.
A recommendation system typically consists of:
- User: People in the system who have preferences for items and people who can be a source of data as well are called Users. Each user may have a set of user attributes, if we are using user demographic (age, gender etc.) then demographic is a user attribute.
- Items : Products the system is choosing to recommend are known as Items . For each item we may have a set of item attributes or properties.
- Preferences : These represent users’ likes and dislikes. User meets item in the preferences space
Along with the service oriented websites, the world wide web is flooded with the online news portal like MSN news, Yahoo News, Google News etc. The problem with these news portal are that they can provide overwhelming volume of news articles which user can digest. Even the global news aggregator like google news do not have high visitor retention rate and provide the user with huge volume of news. The challenge is to find the user with the articles that are interesting to read and engage.