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In this work we studied collaborative filtering on datasets with implicit feedback, which is a very common situation

Collaborative Filtering for Implicit Feedback Datasets

ICDM, pp.263-272, (2008)

Cited by: 2773|Views655
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Abstract

A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively ...More

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Introduction
  • As e-commerce is growing in popularity, an important challenge is helping customers sort through a large variety of offered products to find the ones they will enjoy the most.
  • One of the tools that address this challenge is recommender systems, which are attracting a lot of attention recently [1, 4, 12].
  • These systems provide users with personalized recommendations for products or services, which hopefully suit their unique taste and needs.
Highlights
  • As e-commerce is growing in popularity, an important challenge is helping customers sort through a large variety of offered products to find the ones they will enjoy the most
  • One of the tools that address this challenge is recommender systems, which are attracting a lot of attention recently [1, 4, 12]
  • Recommender systems are based on two different strategies
  • In this work we studied collaborative filtering on datasets with implicit feedback, which is a very common situation
  • One of our main findings is that implicit user observations should be transformed into two paired magnitudes: preferences and confidence levels
Results
  • Evaluation methodology

    The authors evaluate a scenario where the authors generate for each user an ordered list of the shows, sorted from the one predicted to be most preferred till the least preferred one.
  • The first model is sorting all shows based on their popularity, so that the top recommended shows are the most popular ones
  • This naive measure is surprisingly powerful, as crowds tend to heavily concentrate on few of the many thousands available shows.
  • The authors take this as a baseline value.
Conclusion
  • In this work the authors studied collaborative filtering on datasets with implicit feedback, which is a very common situation.
  • For each user-item pair, the authors derive from the input data an estimate to whether the user would like or dislike the item (“preference”) and couple this estimate with a confidence level.
  • This preference-confidence partition has no parallel in the widely studied explicit-feedback datasets, yet serves a key role in analyzing implicit feedback.
  • Taking all user-item values as an input to the model raises serious scalability issues – the number of all those pairs tends to significantly exceed the input size since a typical user would provide feedback
Tables
  • Table1: Three recommendations with explanations for a single user in our study. Each recommended show is recommended due to a unique set of already-watched shows by this user
Download tables as Excel
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