Video: Introduction about Netflix and Recommender systems

eMarketer - Netflix: Personalized Product Recommendations

    This is a youtube clip that gives you a simple introduction about how Netflix uses the collaborative filtering recommender system to improve their business. It conveys some simple ideas and is worth a look. 

Clinical Recommender system

    This is very interesting that the recommender systems can be used in the health care industry. here's the article, Healthcare information systems: data mining methods in the creation of a clinical recommender system. 


    We usually think that the recommender systems are used consumers good such as movies, music, books, and so forth. But right now, recommender systems can be blended in the healthcare information technology. 
    
    For example illustrated in the article, the nursing care plan can use the recommender system to provide clinical decision support, nursing education, clinical quality control, and to be a complement for existing practice guidelines. 

    In the study, they used nursing diagnosis data to develop a methodology, producing a ranked list of suggested care plan based on the information they put in.  

Enterprise Information Systems, May2011, Vol. 5 Issue 2, p169-181, 13p, 2 Diagrams, 3 Charts, 5 Graphs Chart; found on p170

    it's very interesting to see the recommender systems combined with different industry and provide technical assistance to healthcare. It looks like there will be a lot of different applications that recommender systems will implement in our life.

Content-based filtering

    According to Francesco, the author of Recommender System Handbook, content-based filtering is using the technique to analyze a set of documents and descriptions of items previously rated by a user, and then build a profile or model of the users interests based on the features of those rated items. Using the profile, the recommender system can filter out the suggestions that would fit for the user.


     Go deep into the process, there are three steps of the recommendation process.

• Content analyzer: the main responsibility of the process is to represent the content of items. it can extract the information or specific features from the item by feature extraction techniques. 
• Profile learner: this process will collect data representative of the users preferences and try to generalize the data, then construct the user profile. 
• Filtering components: this process will try to match the features of the user profile with the features of the items. And then, the system will recommend items that fit for the user. 

Here is a flow chart of the process captured from Recommender System Handbook.

it tell you how the system processes the information source and user profiles, so that they can recommend items based on the process.

So, compared to collaborative filtering, there are some advantages and drawbacks of content-based filtering that we should understand. 

Advantages

• User independence: collaborative filtering needs other users' rating to find the similarity between the users and then give the suggestion. Instead, content-based method only have to analyze the items and user profile for recommendation.
• Transparency: collaborative method gives you the recommendation because some unknown users have the same taste like you, but content-based method can tell you  they recommend you the items based on what features. 
• No cold start: opposite to collaborative filtering, new items can be suggested before being rated by a substantial number of users. 

Disadvantages

• Limited content analysis: if the content does not contain enough information to discriminate the items precisely, the recommendation will be not precisely at the end.
• Over-specialization: content-based method provides a limit degree of novelty, since it has to match up the features of profile and items. A totally perfect content-based filtering may suggest nothing "surprised." 
• New user: when there's not enough information to build a solid profile for a user, the recommendation could not be provided correctly. 

There are different merits and drawbacks either for collaborative filtering or for content-based filtering. So most of the websites they start to use the hybrid system to combine the advantages of those two method and try to give their customers an easier and more valuable recommendations.

"Recommend" some recommender system based websites

Here are some interesting websites which use recommender systems.

WhatShouldIReadNext.com

You can use the website to help you determine the next book to read. Just add the book title that you enjoyed recently, and the site will give you the recommendation.

Last.fm

This is a radio station that you can listen music online. It uses a recommender system to track what you've listened and suggests music based on your taste.

StumbleUpon

This is a website which can recommend you the websites you may be interested in. It gives you the suggestions based on your ratings of websites.

Netflix

Netflix was a DVD rental delivery and online stream website which uses the recommender system to suggest its users movies or TV series based on their rating data.

Pandora

This is also a music radio website. Users enter their favorite songs and artists into Pandora, then Pandora recommends music similar to your taste. Then you can listen to the new music through the site.

Here is a source for some popular recommender system websites. Check it out.

http://www.deitel.com/ResourceCenters/Web20/RecommenderSystems/PopularRecommenderSystems/tabid/1312/Default.aspx