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Recommendation system 

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). Recommendation systems are rapidly advancing on the Web as an effective way to connect consumers with products and services they would most likely purchase based on their past actions and interests. With recommendation technology, Web properties can become even more dynamic as they deliver direct matching links based on the aggregation of user patterns. [1]

Contents

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[2]
  • 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.[3] Herlocker provides an overview of evaluation techniques for recommender systems.[4]

More recently, a successful recommender system has been introduced for bricks and mortar superstores based upon statistical inference[5] 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.[6]. 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. ^ Internet Evolution (www.internetevolution.com): The New ‘Recommendation Web’
  2. ^ 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 .
  3. ^ 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, <http://portal.acm.org/citation.cfm?id=1070611.1070751> .
  4. ^ 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, <http://portal.acm.org/citation.cfm?id=963772> .
  5. ^ Quatse, Jesse and Najmi, Amir (2007) "Empirical Bayesian Targeting," Proceedings, WORLDCOMP'07, World Congress in Computer Science, Computer Engineering, and Applied Computing.
  6. ^ Sarwar, B.; Karypis, G.; Konstan, J. & Riedl, J. (2000), Application of Dimensionality Reduction in Recommender System A Case Study, <http://glaros.dtc.umn.edu/gkhome/node/122> .

External links


Research Groups

ACM Recommender Systems Series

Journal Special Issues

Workshops

Further reading
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