Difference between revisions of "Recommender Systems"
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===Implications of the use of explicit data in recommender systems=== | ===Implications of the use of explicit data in recommender systems=== | ||
| − | Two examples of sites that use explicit forms of data in their recommender systems include Netflix and commenting on Slashdot. Netflix is a hybrid recommender system, meaning it combines multiple sources of user data to form recommendations, while Slashdot primarily employs a user rankings system to determine the order of commentary displayed on a given post. Slashdot's user generated rankings determines non-personalized recommendations. While this system avoids the ethical concern of information privacy, it does bring up the concern of "buried treasures" or poorly distributed visibility to posts that are worthy of notice. Buried treasure occurs when moderators of Slashdot attend less to comments with lower scores that may have been unfairly rated or neglect to scan over comments posted later in a conversation. This is an ethical concern because by using a recommender system to determine the importance of a comment, there is potential to neglect equally important perspectives that have been unfairly rated. By deciding for users what information is most important to read, a bias towards one perspective of view point can be imposed on a the site's readers. <ref>http://presnick.people.si.umich.edu/papers/chi04/LampeResnick.pdf</ref> | + | Two examples of sites that use explicit forms of data in their recommender systems include [[Netflix]] and commenting on [http://slashdot.org/ Slashdot]. Netflix is a hybrid recommender system, meaning it combines multiple sources of user data to form recommendations, while Slashdot primarily employs a user rankings system to determine the order of commentary displayed on a given post. Slashdot's user generated rankings determines non-personalized recommendations. While this system avoids the ethical concern of information privacy, it does bring up the concern of "buried treasures" or poorly distributed visibility to posts that are worthy of notice. Buried treasure occurs when moderators of Slashdot attend less to comments with lower scores that may have been unfairly rated or neglect to scan over comments posted later in a conversation. This is an ethical concern because by using a recommender system to determine the importance of a comment, there is potential to neglect equally important perspectives that have been unfairly rated. By deciding for users what information is most important to read, a bias towards one perspective of view point can be imposed on a the site's readers. <ref>http://presnick.people.si.umich.edu/papers/chi04/LampeResnick.pdf</ref> |
== See Also == | == See Also == | ||
Revision as of 23:17, 3 December 2012
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.
Many advertising companies are employing technologies such as cookies and spyware in order to learn more about people's Internet browsing history and Internet preferences. These advertising companies can contribute a person's Internet browsing search and give to companies to further refine their recommender systems.
Accountability is another ethical concern when dealing with recommender systems. recommender systems are based off of massive amounts of data but what if the data collected and is incorrect and the recommender is recommending things that could have ethical implications. For example what if a recommender system recommends a food establishment and the customer is unpleased or even harmed. Is the recommender system held accountable for recommending this establishment or is it the programmer or is it the the customers that made the reviews? Should the algorithm be deleted?
Implications of the use of explicit data in recommender systems
Two examples of sites that use explicit forms of data in their recommender systems include Netflix and commenting on Slashdot. Netflix is a hybrid recommender system, meaning it combines multiple sources of user data to form recommendations, while Slashdot primarily employs a user rankings system to determine the order of commentary displayed on a given post. Slashdot's user generated rankings determines non-personalized recommendations. While this system avoids the ethical concern of information privacy, it does bring up the concern of "buried treasures" or poorly distributed visibility to posts that are worthy of notice. Buried treasure occurs when moderators of Slashdot attend less to comments with lower scores that may have been unfairly rated or neglect to scan over comments posted later in a conversation. This is an ethical concern because by using a recommender system to determine the importance of a comment, there is potential to neglect equally important perspectives that have been unfairly rated. By deciding for users what information is most important to read, a bias towards one perspective of view point can be imposed on a the site's readers. [3]
See Also
- Data Aggregation Online
- Data Mining
- Geographic Information Systems
- Information Ethics
- Information Transparency
- Online Dating: Recommender Systems
- Privacy in Social Networking
- Privacy in the Online Environment
- Social Networking
- Targeted Advertising (Online)
References
- ↑ Wikipedia:Recommender_system
- ↑ http://www.childrensmercy.org/stats/definitions/correlation.htm
- ↑ http://presnick.people.si.umich.edu/papers/chi04/LampeResnick.pdf
