This blog post will give you a whirlwind tour of Recommendation Systems. Recommendation systems (sometimes referred to as a recommender system) are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that a user would give to an item.
Recommender systems are one of the most common applications of Big Data. They are of special interest to organizations who wants to analyze user choices and personalize the content for other users. Most of us have experienced the power of personalized recommendations firsthand. Maybe you found former colleagues and classmates with LinkedIn’s “People You May Know” feature. Perhaps you watched a movie because Netflix suggested it to you. And you’ve most likely bought something that Amazon recommended under “Frequently Bought Together” or “Customers Who Bought This.” Recommendation engines account for a huge share of revenue and user activity.
A couple of years ago, Alex Iskold outlined what he saw as the 4 main approaches to recommendations:
- Personal Recommendation : Recommend things based on the individuals past behavior
- Social Recommendation: Recommend things based on the past behavior of similar users
- Item Recommendation : Recommend things based on the item itself
- A combination of the three approaches above
However, Recommendation systems can be based on two different types of algorithm
- Collaborative Filtering: Also referred to as social filtering, filters information by using the recommendations of other people. It is based on the idea that people who agreed in their evaluation of certain items in the past are likely to agree again in the future.
- Item-Based method: recommender engines compute the similarity of items. Item-based recommender engine is presented with a user and a set of his/her preferences for some items. It will compute other items similar to the preferred items and recommend the closest matches
- User-Based method: recommender engines compute the similarity of users. If a user-based recommender engine is provided the same inputs, it will compute the most similar other users and recommend the most preferred items as expressed by those similar users.
- Content Based Filtering: The system uses a detailed profile of what a user has previously bought, liked, searched for, tweeted about, blogged about, visited etc. Based on that information, a profile is created and products are recommended that best fit that profile based on the attributes of their product
Recommender systems are rapidly becoming a crucial tool in E-commerce on the Web. These systems help users find items they want to buy from a business. Recommender systems benefit users by enabling them to find items they like. Conversely, they help the business by generating more sales. Recommender systems are being stressed by the huge volume of user data in existing corporate databases, and will be stressed even more by the increasing volume of user data available on the Web. Hence, new technologies are needed that can dramatically improve the scalability of recommender systems.
