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Recommender system

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Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach).

Overview

When building the user's profile a distinction is made between explicit and implicit forms of data collection.

Examples of explicit data collection include the following:

  • Asking a user to rate an item on a sliding scale.
  • Asking a user to rank a collection of items from favorite to least favorite.
  • Presenting two items to a user and asking him/her to choose the best one.
  • Asking a user to create a list of items that he/she likes.

Examples of implicit data collection include the following:

  • Observing the items that a user views in an online store.
  • Analyzing item/user viewing times[1]
  • Keeping a record of the items that a user purchases online.
  • Obtaining a list of items that a user has listened to or watched on his/her computer.
  • Analyzing the user's social network and discovering similar likes and dislikes

The recommender system compares the collected data to similar data collected from others and calculates a list of recommended items for the user. Several commercial and non-commercial examples are listed in the article on collaborative filtering systems. Adomavicius provides an overview of recommender systems.[2] Herlocker provides an overview of evaluation techniques for recommender systems.[3]

More recently, a successful recommender system has been introduced for bricks and mortar superstores based upon statistical inference[4] as opposed to the Collaborative Filtering techniques of eCommerce. Redemption rates, or "hit rates," are much higher averaging as much as 45% in chain grocery stores.

Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves. Interestingly enough, recommender systems are often implemented using search engines indexing non-traditional data.

Recommender systems are also sometimes known colloquially as "Gilligans".

Algorithms

One of the most commonly used algorithms in recommender systems is Nearest Neighborhood approach.[5]. In a social network, a particular user's neighborhood with similar taste or interest can be found by calculating Pearson Correlation, by collecting the preference data of top-N nearest neighbors of the particular user, the user's preference can be predicted by calculating the data using certain techniques.

Examples

See also

References

  1. ^ Parsons, J.; Ralph, P.; Gallagher, K. (July 2004), Using viewing time to infer user preference in recommender systems., AAAI Workshop in Semantic Web Personalization, San Jose, California {{citation}}: Unknown parameter |month= ignored (help)CS1 maint: date and year (link).
  2. ^ Adomavicius, G.; Tuzhilin, A. (June 2005), "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions", IEEE Transactions on Knowledge and Data Engineering, 17 (6): 734–749, doi:10.1109/TKDE.2005.99, ISSN 1041-4347 {{citation}}: Unknown parameter |month= ignored (help)CS1 maint: date and year (link).
  3. ^ Herlocker, J. L.; Konstan, J. A.; Terveen, L. G.; Riedl, J. T. (January 2004), "Evaluating collaborative filtering recommender systems", ACM Trans. Inf. Syst., 22 (1): 5–53, doi:10.1145/963770.963772, ISSN 1046-8188 {{citation}}: Unknown parameter |month= ignored (help)CS1 maint: date and year (link).
  4. ^ Quatse, Jesse and Najmi, Amir (2007) "Empirical Bayesian Targeting," Proceedings, WORLDCOMP'07, World Congress in Computer Science, Computer Engineering, and Applied Computing.
  5. ^ Sarwar, B.; Karypis, G.; Konstan, J.; Riedl, J. (2000), Application of Dimensionality Reduction in Recommender System A Case Study.


Research Groups

ACM Recommender Systems Series

Journal Special Issues

Workshops

Further reading

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