Name
Abstract | ||
|---|---|---|
Abandonment in web search has been widely used as a proxy
to measure user satisfaction. Initially it was considered a
signal of dissatisfaction, however with search engines moving
towards providing answer-like results, a new category of
abandonment was introduced and referred to as Good Abandonment.
Predicting good abandonment is a hard problem
and it was the subject of several previous studies. All those
studies have focused, though, on predicting good abandonment
in offline settings using manually labeled data. Thus,
it remained a challenge how to have an online metric that
accounts for good abandonment. In this work we describe
how a search success metric can be augmented to account for
good abandonment sessions using a machine learned metric
that depends on user’s viewport information. We use real
user traffic from millions of users to evaluate the proposed
metric in an A/B experiment. We show that taking good
abandonment into consideration has a significant effect on
the overall performance of the online metric. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2983323.2983867 | ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
good abandonment,search,metric | Proxy (climate),Data mining,Search engine,Information retrieval,Viewport,Computer science,Labeled data | Conference |
ISBN | Citations | PageRank |
978-1-4503-4073-1 | 1 | 0.35 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madian Khabsa | 1 | 237 | 18.81 |
| Aidan Crook | 2 | 1 | 0.35 |
| Ahmed Hassan | 3 | 943 | 57.64 |
| Imed Zitouni | 4 | 1 | 1.02 |
| Tasos Anastasakos | 5 | 412 | 56.01 |
| Kyle Williams | 6 | 208 | 21.61 |