Recommender Systems : Software that find / match the interests and preferences of individual consumer’s, based on past searches, wish list, similar search to other costumers, products in same category, etc.

They have the potential to support and improve the quality of the decisions consumers make while searching for and selecting products online.

Recommender systems typically produce a list of recommendations in one of two ways:

 

Collaborative: approaches building a model from a user’s past behaviour, items previously purchased or selected, items as well as similar decisions made by other users.

  • This method is based on collecting and analysing a large amount of information on users’ behaviours
  • Activities or preferences and predicting what users will like based on their similarity to other users.
  • It does not rely on machine analysable content and therefore it is capable of accurately recommending complex items, without knowing the content of product

 

Content-based filtering: approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.

  • This method is based on a description of the item and a profile of the user’s preference
  • Keywords are used to describe the items and a user profile is built to indicate the type of item this user likes
  • Algorithms try to recommend items that are similar to those that a user liked in the past searches, views

 

 

Hybrid recommender systems: combining collaborative filtering and content-based filtering

Make recommendations by comparing the purchase/watching and searching habits of similar users (using collaborative method) well as by offering videos/products that share characteristics with items that a user has rated highly (using content-based method).

(Dataconomy, 2015)

 

 

 

Ref :

Dataconomy. (2015). An Introduction to Recommendation Engines – Dataconomy. [online] Available at: http://dataconomy.com/an-introduction-to-recommendation-engines/.

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