Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximating Categorical Similarity in Sponsored Search Relevance   Sponsored Search is a major source of revenue for web search engines. Since
sponsored search follows a pay-per-click model, showing relevant ads for
receiving clicks is crucial. Matching categories of a query and its ad
candidates have been explored in modeling relevance of query-ad pairs. The
approach involves matching cached categories of queries seen in the past to
categories of candidate ads. Since queries have a heavy tail distribution, the
approach has limited coverage. In this work, we propose approximating
categorical similarity of a query-ad pairs using neural networks, particularly
CLSM. Embedding of a query (or document) is generated using its tri-letter
representation which allows coverage of tail queries. Offline experiments of
incorporating this feature as opposed to using the categories directly show a
5.23% improvement in AUC ROC. A/B testing results show an improvement of 8.2%
in relevance.

30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an