This is the presentation for my work that I presented at DASFAA, 2017 in Suzhou, China.

1. An improved approach for long
tail advertising in Sponsored
Search
Amar Budhiraja, P. Krishna Reddy
International Institute of Information Technology,
Hyderabad, India.

2. What is Sponsored Search?

3. How does Sponsored Search works?
Advertisers select
Keywords relevant
to their ads
Incoming
Search Query
Bidding
&
Ranking
of
Ads
Display of Ads

4. Research Issue
Long tail distribution of a small but fat head
of frequent queries and a long-thin tail of
infrequent queries
Advertising on tail queries is challenging
as tail queries are encountered rarely
Advertisers tend to bid for the head query
keywords
Creates a high demand for the head
query keywords and little to no
demand for the tail query keywords

5. Background: Coverage Patterns
A Coverage pattern (CP) is a set of items
that can cover a certain percentage of
transactions in a transactional database
which satisfies thresholds of the three
parameters:
minRF: Minimum Relative Frequency
minCS: Minimum Coverage Support
maxOR: Maximum Overlap Ratio

6. Basic Idea
Let advertisers bid upon high level
concepts instead of keywords through a
taxonomy
Keywords would be then considered
with respect to their relevancy rather
than frequency
Reduces load on advertisers to find
“all” relevant search keywords

7. Proposed Approach
Each Advertiser is shown a taxonomy based on the landing page of his/her ad
An advertiser is asked to a select that represents his/her product
An estimated allocation model is proposed
Only groups immediate children nodes of bidding node are allocated to advertisers to acknowledge
the amount of generalization requested
To create such combinations of children nodes, notion of Coverage Patterns is employed
However, extraction of CPs considering a taxonomy over the items has not been
proposed.

8. Proposed Approach: T-Cmine

9. Proposed Architecture

10. Proposed Architecture: Allocation Mechanism
Bottom-up allocation is proposed.
However, matching extracted CPs and advertisers is challenging as there is a flow of
coverage in the taxonomy.
Allocation at a node should take into account if any of its descendants have been
allocated as coverage of a node is sum of coverage of its descendants.
CP.imp = CP.imp - ∑k ∑ij Aij

11. Proposed Architecture: Matching CPs and Ads
Objective Function for Matching of CPs and Ads:
Min Z = ∑level, d to 1 (∑j |CPj.impressions - ∑i Adij.impressions|)
s.t. CPj.impressions >= ∑i=1..n(Adij.impressions)

12. Proposed Architecture: Example Allocation

13. Experiments: Dataset
CABS120k08 dataset - A collection of search queries from the AOL500k
dataset along with the documents clicked, document rank, timestamps
and user id.
Advertising demands: Simulated-
Five sets of advertisers having 10, 20, 30, 40 and 50 advertisers.
CPM model and number of requested impressions is randomly chosen
between 100 and 1000.
Bidding:
Keywords based approach: Proportional to keyword frequency
Concept based approach: Average of bids on all the keywords in the ad
campaign.

14. Experiments: Performance Metrics
Average number of unique Advertisements per Session:
To measure ad space utilization
Sessions per Advertisement: To measure the reach of an
advertisement

15. Experiments: Results - Ad Space Utilization
Taxonomy: Arts
Taxonomy: Health
Taxonomy: Shopping Taxonomy: Society

16. Experiments: Results - Reach of Ads
Taxonomy: Arts
Taxonomy: Health
Taxonomy: Shopping Taxonomy: Society

17. Related Work
1.Long tail advertising has been addressed primarily by means of query
expansion.
2.The authors propose an approach in [1] to expand tail queries in real time using
an inverted index built from head and torso expanded queries. Using the
expanded queries, the authors show improvement in ad retrieval.
3.In [2], the authors proposed to use coverage for creating and selling sets of
keywords during keyword auctions.
4.For sponsored search, an architecture has been proposed in [3]. The authors
modelled sponsored search as an online bipartite matching problem with
advertisers are one set of disjoint vertices and queries are the other.
5.It should be noted that the previous approaches have emphasized on keyword
analysis or query expansion where in this paper we present an alternative
approach of bidding on concepts rather than keywords in sponsored search.
6.

18. Conclusions
In this paper, we have studied the feasibility of bidding on concepts in sponsored
search.
To address the issues of inter-dependency of concepts on each other, we exploit
search query logs and a taxonomy to extract level-wise coverage patterns.
The corresponding architecture is used to perform allocation of incoming queries
to advertisers for sponsored search.
Experiments on a real world dataset of AOL search query logs show improvement
in performance with respect to ad space utilization and reach of the
advertisements.

19. Future Work
We plan to analyse what is the trade-off between relevance and bidding on
concepts in terms of targeted advertising.
We plan to investigate how different taxonomies would suit the problem and if it is
possible to build a taxonomy to suit sponsored search so to avoid the long tail
phenomenon amongst the nodes of the taxonomy.
We also intend to look at truthful auctions for concept-based bidding as the
advertisers are targeting same keywords but using different concepts

20. Future Work
Microsoft Research India for supporting the travel to Suzhou, China for presenting this work at DASFAA –
International Conference on DAtabase Systems For Advanced Analytics.