Recommending interesting content to engage users is important for web portals (e.g. AOL, MSN, Yahoo!, and many others). Existing approaches typically recommend articles to optimize for a single objective, i.e., number of clicks. However a click is only the starting point of a user's journey and subsequent downstream utilities such as time-spent and revenue are important. In this paper, we call the problem of recommending links to jointly optimize for clicks and post-click downstream utilities click shaping. We propose a multi-objective programming approach in which multiple objectives are modeled in a constrained optimization framework. Such a formulation can naturally incorporate various applicationdriven requirements. We study several variants that model different requirements as constraints and discuss some of the subtleties involved. We conduct our experiments on a large dataset from a real system by using a newly proposed unbiased evaluation methodology [17] . Through extensive experiments we quantify the tradeoff between different objectives under various constraints. Our experimental results show interesting characteristics of different formulations and our findings may provide valuable guidance to the design of recommendation engines for web portals.
Yahoo! proposes a framework to optimize CTR and post-click engagement of the articles REF .
Click shaping to optimize multiple objectives
KDD
Computer Science