Abstract
In this paper, we discuss the importance of different types of implicit user feedback for creating useful recommendations on an e-commerce website. Each website user may provide us with many different types of implicit feedback and it is difficult to decide which one to use for recommendations.
If our recommendation algorithm support using more implicit factors, we should also consider importance and "added value" of each factor. We have identified several widely used implicit factors and conducted real user online testing in order to compare their usefulness for recommending algorithms.
We have also proposed some combinations of implicit factors and a test, to see if they improve recommendation performance in comparison with the single factor ones.