What are recommender systems, how are they developed, and how is the data used to build a recommendation system obtained?

What will be an ideal response?

• The term recommender systems refers to a Web-based information filtering system that takes the inputs from users and then aggregates the inputs to provide recommendations for other users in their product or service selection choices.
• Two basic approaches that are employed in the development of recommendation systems are collaborative filtering and content filtering.
In collaborative filtering, the recommendation system is built based on the individual user's past behavior by keeping track of the previous history of all purchased items. This includes products, items that are viewed most often, and ratings that are given by the users to the items they purchased.
In the content-based filtering approach, the characteristics of an item are profiled first and then content-based individual user profiles are built to store the information about the characteristics of specific items that the user has rated in the past. In the recommendation process, a comparison is made by filtering the item information from the user profile for which the user has rated positively and compares these characteristics with any new products that the user has not rated yet. Recommendations are made if there are similarities found in the item characteristics.
• The data necessary to build a recommendation system are collected by Web-based systems where each user is specifically asked to rate an item on a rating scale, rank the items from most favorite to least favorite, and/or ask the user to list the attributes of the items that the user likes.