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The appropriate metric to choose may depend on the type of items being recommended, the user tasks supported by the collaborative filtering system, and any external goals that the service providers may have

Collaborative filtering recommender systems

The Adaptive Web, pp.291-324, (2007)

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摘要

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substanti...更多

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简介
  • Collaborative Filtering is the process of filtering or evaluating items using the opinions of other people.
  • Amy might observe that Matt recommends the types of films that she finds enjoyable, Paul has a history of recommending films that she despises, and Margaret just seems to recommend everything.
  • Over time, she learns whose opinions she should listen to and how these opinions can be applied to help her determine the quality of an item
重点内容
  • Collaborative Filtering is the process of filtering or evaluating items using the opinions of other people
  • There is no well-accepted metric that can evaluate all-important criteria related to the performance of a collaborative filtering system
  • The appropriate metric to choose may depend on the type of items being recommended, the user tasks supported by the collaborative filtering system, and any external goals that the service providers may have
  • Predictive accuracy is the ability of a collaborative filtering system to predict a user's rating for an item
  • The standard method for computing predictive accuracy is mean absolute error (MAE) – the average absolute difference between the predicted rating and the actual rating given by a user
  • How can collaborative filtering algorithms be applied to tags? Can tags be used in conjunction naturally with ratings?
结果
  • Evaluation measures how well a collaborative filtering system is meeting its goals, either in absolute terms or in relation to alternative CF systems.
  • The most prominent evaluation metrics in the research literature measure the accuracy of the system's predictions.
  • Predictive accuracy is the ability of a collaborative filtering system to predict a user's rating for an item.
  • Social systems such as flickr and del.icio.us, which allow users to tag things with keywords, are increasing in popularity and have captured the imagination of many people
  • These are collaborative filtering systems surely, though without much automation as yet.
  • How can collaborative filtering algorithms be applied to tags? Can tags be used in conjunction naturally with ratings?
结论
  • Collaborative filtering is one of the core technologies that will power the adaptive web.
  • In order to filter information based on such complex dimensions, the authors need to include people in the loop, who analyze the information and condense their opinions into data that can be processed by software – ratings.
  • The authors have attempted to provide a snapshot of the current understanding of collaborative filtering systems and methods.
  • The authors will continue to gain a deeper understanding of the dynamics of collaborative filtering
表格
  • Table1: A MovieLens ratings matrix. Amy rated the movie Sideways a 5. Matt has not seen The Matrix
  • Table2: Most common explicit rating scales
Download tables as Excel
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