  Traditionally the probabilistic ranking principle is used to rank the search
results while the ranking based on expected profits is used for paid placement
of ads. These rankings try to maximize the expected utilities based on the user
click models. Recent empirical analysis on search engine logs suggests a
unified click models for both ranked ads and search results. The segregated
view of document and ad rankings does not consider this commonality. Further,
the used models consider parameters of (i) probability of the user abandoning
browsing results (ii) perceived relevance of result snippets. But how to
consider them for improved ranking is unknown currently. In this paper, we
propose a generalized ranking function---namely "Click Efficiency (CE)"---for
documents and ads based on empirically proven user click models. The ranking
considers parameters (i) and (ii) above, optimal and has the same time
complexity as sorting. To exploit its generality, we examine the reduced forms
of CE ranking under different assumptions enumerating a hierarchy of ranking
functions. Some of the rankings in the hierarchy are currently used ad and
document ranking functions; while others suggest new rankings. While optimality
of ranking is sufficient for document ranking, applying CE ranking to ad
auctions requires an appropriate pricing mechanism. We incorporate a second
price based pricing mechanism with the proposed ranking. Our analysis proves
several desirable properties including revenue dominance over VCG for the same
bid vector and existence of a Nash Equilibrium in pure strategies. The
equilibrium is socially optimal, and revenue equivalent to the truthful VCG
equilibrium. Further, we relax the independence assumption in CE ranking and
analyze the diversity ranking problem. We show that optimal diversity ranking
is NP-Hard in general, and that a constant time approximation is unlikely.
