- A Recommender Systems is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item.
- Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.
- There are also recommender systems for experts, collaborators, restaurants, garments, financial services, life insurance and twitter pages.
- Most recommender systems focus on the task of information filtering, which deals with the delivery of items selected from a large collection that the user is likely to find interesting or useful.
- Recommender systems are special types of information filtering systems that suggest items to users. Some of the largest e-commerce sites are using recommender systems and apply a marketing strategy that is referred to as mass customization.
- Some web sites present users with personalized information by letting them choose from a set of predefined topics of interest.
- Users however do not always know what they are interested in beforehand and their interests may change overtime which would require them to change their selection frequently.
- Recommender systems provide personalized information by learning the user’s interests from traces of interaction with that user.
- Recommender systems typically produce a list of recommendations in one of two ways – through collaborative filtering or through content-based filtering.
- Collaborative filtering approaches build a model from a user's past behaviour (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items that the user may have an interest in.
- Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.
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- Most recommender systems focus on the task of information filtering, which deals with the delivery of items selected from a large collection that the user is likely to find interesting or useful.
- Recommender systems are special types of information filtering systems that suggest items to users. Some of the largest e-commerce sites are using recommender systems and apply a marketing strategy that is referred to as mass customization.
- Some web sites present users with personalized information by letting them choose from a set of predefined topics of interest.
- Users however do not always know what they are interested in beforehand and their interests may change overtime which would require them to change their selection frequently.
- Recommender systems provide personalized information by learning the user’s interests from traces of interaction with that user.
- Recommender systems typically produce a list of recommendations in one of two ways – through collaborative filtering or through content-based filtering.
- Collaborative filtering approaches build a model from a user's past behaviour (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items that the user may have an interest in.
- Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.
STAY UPDATED FOR MORE POST!
SHARE IT WITH YOUR FRIENDS AND DON'T FORGET TO COMMENT BELOW!↓
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