Difference between revisions of "Recommender Systems"
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[[File:Amazon-recommendations.jpg|200px|thumb|right|Amazon's Recommendations for a User]] | [[File:Amazon-recommendations.jpg|200px|thumb|right|Amazon's Recommendations for a User]] | ||
| − | '''Recommender systems''' use collaborative filtering systems to assess a user's likes, dislikes, even loves and hatreds to make suggestions as to potential other product that the user might like besides the page he is actually viewing. The most notable reputation systems include Amazon, Netflix, Pandora Radio, and iTunes. | + | '''Recommender systems''' use collaborative filtering systems to assess a user's likes, dislikes, even loves and hatreds to make suggestions as to potential other product that the user might like besides the page he is actually viewing. The most notable reputation systems include [[www.Amazon.com|Amazon]], [[www.Netflix.com|Netflix]], [[www.PandoraRadio.com|Pandora Radio]], and [[www.iTunes.com|iTunes]]. |
| + | == Overview == | ||
| + | Recommender systems typically provide users with recommendations from two different approaches, [[Wikipedia:collaborative filtering|collaborative filtering]] and [[Wikipedia:content filtering|content filtering]]. Collaborative filtering employs a users past purchase history (as well as users who have similar purchasing histories) to make recommendations on related products. Content filtering employs different features of previously purchased products to recommend other products with similar features. | ||
| − | + | When choosing between these models, a decision between gathering explicit and implicit data on users must be made. | |
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| + | Examples of explicit data gathering include the following<ref>http://en.wikipedia.org/wiki/Recommender_system</ref>: | ||
| + | *Asking a user to rate a particular item. | ||
| + | *Asking a user to rank a collection of items. | ||
| + | *Presenting two items to a user and having them make a choice between the two. | ||
| + | *Asking a user to create a list of items that they enjoy. | ||
| + | Examples of implicit data gathering include the following: | ||
| + | *Observing the items that a user looks at in online environments. | ||
| + | *Keeping track of the items that a user purchases/looks at. | ||
| + | *Analyzing the users social network and looking at what others have purchased and/or liked/disliked. | ||
| + | == Algorithms == | ||
| + | ===K-Nearest Neighbor=== | ||
| + | The k-nearest neighbor (or k-NN) approach uses a majority vote from an objects neighbors to classify said object. | ||
| + | ===Pearson Correlation=== | ||
| + | The Pearson correlation <ref>http://www.childrensmercy.org/stats/definitions/correlation.htm</ref>uses social networks to determine a users interests from the interests of those that they are associated with. | ||
== Ethical Issues == | == Ethical Issues == | ||
| + | Privacy is a large ethical concern with recommender systems, because the data (whether it is explicit or implicit) that is gathered on users is done oftentimes without the users consent and without the users awareness. There are ways to trace large sets of data gathered from thousands of users back to individuals and this can result in private information (such as credit card number, social security numbers, etc) to be discovered as well. It is important that as new recommender systems are developed, and more specifically new algorithms to run these systems, ethical implications are kept in mind and included in the development of these products. | ||
== References == | == References == | ||
| + | <references/> | ||
Revision as of 17:01, 18 December 2011
Recommender systems use collaborative filtering systems to assess a user's likes, dislikes, even loves and hatreds to make suggestions as to potential other product that the user might like besides the page he is actually viewing. The most notable reputation systems include Amazon, Netflix, Pandora Radio, and iTunes.
Contents
Overview
Recommender systems typically provide users with recommendations from two different approaches, collaborative filtering and content filtering. Collaborative filtering employs a users past purchase history (as well as users who have similar purchasing histories) to make recommendations on related products. Content filtering employs different features of previously purchased products to recommend other products with similar features.
When choosing between these models, a decision between gathering explicit and implicit data on users must be made.
Examples of explicit data gathering include the following[1]:
- Asking a user to rate a particular item.
- Asking a user to rank a collection of items.
- Presenting two items to a user and having them make a choice between the two.
- Asking a user to create a list of items that they enjoy.
Examples of implicit data gathering include the following:
- Observing the items that a user looks at in online environments.
- Keeping track of the items that a user purchases/looks at.
- Analyzing the users social network and looking at what others have purchased and/or liked/disliked.
Algorithms
K-Nearest Neighbor
The k-nearest neighbor (or k-NN) approach uses a majority vote from an objects neighbors to classify said object.
Pearson Correlation
The Pearson correlation [2]uses social networks to determine a users interests from the interests of those that they are associated with.
Ethical Issues
Privacy is a large ethical concern with recommender systems, because the data (whether it is explicit or implicit) that is gathered on users is done oftentimes without the users consent and without the users awareness. There are ways to trace large sets of data gathered from thousands of users back to individuals and this can result in private information (such as credit card number, social security numbers, etc) to be discovered as well. It is important that as new recommender systems are developed, and more specifically new algorithms to run these systems, ethical implications are kept in mind and included in the development of these products.
