Knowledge bases of entities and relations (either constructed manually or
automatically) are behind many real world search engines, including those at
Yahoo!, Microsoft, and Google. Those knowledge bases can be viewed as graphs
with nodes representing entities and edges representing (primary)
relationships, and various studies have been conducted on how to leverage them
to answer entity seeking queries. Meanwhile, in a complementary direction,
analyses over the query logs have enabled researchers to identify entity pairs
that are statistically correlated.
We present a formal model for studying fashion trends, in terms of three
parameters of fashionable items: (1) their innate utility; (2) individual
boredom associated with repeated usage of an item; and (3) social influences
associated with the preferences from other people. While there are several
works that emphasize the effect of social influence in understanding fashion
trends, in this paper we show how boredom plays a strong role in both
individual and social choices.
In this paper, we identify a fundamental algorithmic problem that we term
space-constrained dynamic covering (SCDC), arising in many modern-day web
applications, including ad-serving and online recommendation systems in eBay
and Netflix. Roughly speaking, SCDC applies two restrictions to the
well-studied Max-Coverage problem: Given an integer k, X={1,2,...,n} and
I={S_1, ..., S_m}, S_i a subset of X, find a subset J of I, such that |J| <= k
and the union of S in J is as large as possible.
The Web has enabled the availability of a huge amount of useful information,
but has also eased the ability to spread false information and rumors across
multiple sources, making it hard to distinguish between what is true and what
is not. Recent examples include the premature Steve Jobs obituary, the second
bankruptcy of United airlines, the creation of Black Holes by the operation of
the Large Hadron Collider, etc.