Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deeply Supervised Semantic Model for Click-Through Rate Prediction in
  Sponsored Search   In sponsored search it is critical to match ads that are relevant to a query
and to accurately predict their likelihood of being clicked. Commercial search
engines typically use machine learning models for both query-ad relevance
matching and click-through-rate (CTR) prediction. However, matching models are
based on the similarity between a query and an ad, ignoring the fact that a
retrieved ad may not attract clicks, while click models rely on click history,
being of limited use for new queries and ads. We propose a deeply supervised
architecture that jointly learns the semantic embeddings of a query and an ad
as well as their corresponding CTR.We also propose a novel cohort negative
sampling technique for learning implicit negative signals. We trained the
proposed architecture using one billion query-ad pairs from a major commercial
web search engine. This architecture improves the best-performing baseline deep
neural architectures by 2\% of AUC for CTR prediction and by statistically
significant 0.5\% of NDCG for query-ad matching.

18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an