Recommendation system - benefits and drawbacks

Recommendation systems play a viral role in today's web. Here are some of the benefits and drawbacks of recommendation systems.

• Recommendation systems are based on actual user behavior i.e. objective reality. This is the biggest advantage - watching people in their natural environment and making design decisions directly on the results. For example, the "Suggested Post" feature of Facebook suggests posts based on our activity and likes.
• Recommendation systems are great for discovery. For example, the "Genius Recommendations" feature of iTunes, "Frequently Bought Together" of Amazon.com makes surprising recommendations which are similar to what we already like. The "Now Touching The Void and Into Thin Air" example discussed in class is a best example. 
• Recommendation systems are effective tools for personalization. We often take recommendations from friends and family because we trust their opinion. They know what we like better than anyone else. This is the sole reason they are good at recommending things. This is what recommendation systems try to model. 
• Recommendation systems are always up-to-date. A new product in Amazon gets recommended as long as people rate it highly. The ability for a recommendation system to bubble up activity in real time is a huge advantage because the system is always on.
• Most of the organizational maintenance of a site is keeping the navigation system in line with the users' changing needs. With recommendation systems, organizational maintenance is reduced. Based on user activity, the system recommends navigation options to the user. It still takes a designer to decide what type of information should be displayed on what screen. This introduces a drawback too. Keeping the system up and running becomes a major task. So maintenance has to be shifted elsewhere.
• Recommendation systems are intensive, database-driven applications that are difficult to set up and get running.
• Sometimes recommendation systems are wrong which makes people unhappy. Here are two cases where recommendation systems went awry. In 2005, Wal-Mart's movie recommendation system recommended movies to users in an inappropriate. Amazon started promoting their new clothing site by recommending clothes to users shopping for DVDs. 
• One drawback associated to news recommendation sites such as Digg is "gaming the system". When a news story is popular, it gets promoted to home page as a recommendation for everyone to  click on and read. This leads to thousands of users clicking on the article and reading it. This huge increase in attention is attractive to people willing to game the system for their own personal benefit.

Reference: http://www.uie.com/articles/recommendation_systems/