Collaborative amazon.com. Amazon’s item recommendation system is based

Collaborative Filtering techniques explore the idea that relationships exists between products and people’s interests. Many recommendation systems, also called recommender

systems, use Collaborative Filtering to realize these relationships and to give an accurate

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recommendation of a product that the user may like or enjoy. Collaborative Filtering bases these relationships on choices that a user makes when buying, watching, or enjoying something. Then makes connections with other users of similar interests to produce a prediction.

 

One popular example of Collaborative Filtering is Netflix. Everything on their site is driven by their customer’s selections, which if made frequently enough, get turned into recommendations. Netflix orders these recommendations in such a way that the highest ranking items are more visible to users, in hopes of getting them

to select those recommendations as well.

 

Another popular example is amazon.com. Amazon’s item recommendation system is based on what you’ve previously purchased, as well as the frequency with which you’ve looked at certain books or other items during previous visits to their website. The advantages of using Collaborative Filtering is that users get a broader exposure to many different products they might be interested in. This exposure encourages users towards continual usage or purchase of their product. Not only does this provide a better experience for the user, but it benefits the service provider as well, with increased potential revenue and better security for its consumers.

 

There are some Challenges with Collaborative Filtering. One of them is Data Sparsity. Having a Large Dataset will most likely result in a user-item matrix being large and sparse, which may provide a good level of accuracy, but also pose a risk to speed. In comparison, having a small dataset would result in faster speeds but lower accuracy. Another issue to keep in mind is something called ‘cold start’. This is where new users do not have a sufficient amount of ratings to give an accurate recommendation.