Difference between revisions of "Implicit feedback"
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'''Implicit feedback''' is [[user]] activity that can be used to indirectly infer user [[preference]]s, e.g. clicks, page views, purchase actions. Sometimes only positive feedback is known, e.g. the products customers have bought, but not the ones they have decided against. | '''Implicit feedback''' is [[user]] activity that can be used to indirectly infer user [[preference]]s, e.g. clicks, page views, purchase actions. Sometimes only positive feedback is known, e.g. the products customers have bought, but not the ones they have decided against. | ||
== Prediction from Implicit Feedback == | == Prediction from Implicit Feedback == | ||
| − | Prediction from implicit feedback(implicit feedback ranking) | + | Prediction from implicit feedback (implicit feedback ranking) |
| − | refers to the task that ranks a list of item so that the items user prefer( more likely to click,view etc. ) will be in higher order. | + | refers to the task that ranks a list of item so that the items user prefer (more likely to click, view etc.) will be in higher order. |
== Using Implicit Feedback == | == Using Implicit Feedback == | ||
Latest revision as of 04:53, 12 November 2011
Implicit feedback is user activity that can be used to indirectly infer user preferences, e.g. clicks, page views, purchase actions. Sometimes only positive feedback is known, e.g. the products customers have bought, but not the ones they have decided against.
Prediction from Implicit Feedback
Prediction from implicit feedback (implicit feedback ranking) refers to the task that ranks a list of item so that the items user prefer (more likely to click, view etc.) will be in higher order.
Using Implicit Feedback
Implicit feedback information can be used to enhance collaborative filtering algorithms for rating prediction. The most famous example is SVD++
See also
- The MyMediaLite software supports item recommendation from implicit feedback.
- The SVDFeature software supports prediction from implicit feedback and using implicit feedback to improve rating prediction.
