Abstrakt
In this paper, we focus on the situation of a typical e-commerce portal employing personalized recommendation. Such website could, in addition to the explicit feedback, monitor many different patterns of implicit user behavior - implicit factors.
The problem arises while trying to infer connections between observed implicit behavior and user preferences - while some connections are obvious, others may not. We have selected several often used implicit factors and conducted online experiment on travel agency web site to find out which implicit factors could replace explicit ratings and (if there are more of them) how to combine their values.
As utility functions determining recommending efficiency was selected click through rate and conversions rate. Our experiments corroborate importance of considering more implicit factors and their different weights.
The best individual results were achieved by means of the scrolling factor, the best combination was Priorfito method (lexicographical ordering based on factor values).