17 research outputs found
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Sponsored search represents a major source of revenue for web search engines.
This popular advertising model brings a unique possibility for advertisers to
target users' immediate intent communicated through a search query, usually by
displaying their ads alongside organic search results for queries deemed
relevant to their products or services. However, due to a large number of
unique queries it is challenging for advertisers to identify all such relevant
queries. For this reason search engines often provide a service of advanced
matching, which automatically finds additional relevant queries for advertisers
to bid on. We present a novel advanced matching approach based on the idea of
semantic embeddings of queries and ads. The embeddings were learned using a
large data set of user search sessions, consisting of search queries, clicked
ads and search links, while utilizing contextual information such as dwell time
and skipped ads. To address the large-scale nature of our problem, both in
terms of data and vocabulary size, we propose a novel distributed algorithm for
training of the embeddings. Finally, we present an approach for overcoming a
cold-start problem associated with new ads and queries. We report results of
editorial evaluation and online tests on actual search traffic. The results
show that our approach significantly outperforms baselines in terms of
relevance, coverage, and incremental revenue. Lastly, we open-source learned
query embeddings to be used by researchers in computational advertising and
related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
Generalized Second Price Auction with Probabilistic Broad Match
Generalized Second Price (GSP) auctions are widely used by search engines
today to sell their ad slots. Most search engines have supported broad match
between queries and bid keywords when executing GSP auctions, however, it has
been revealed that GSP auction with the standard broad-match mechanism they are
currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g.,
its theoretical properties are known only for the single-slot case and
full-information setting, and even in this simple setting, the corresponding
worst-case social welfare can be rather bad). To address this issue, we propose
a novel broad-match mechanism, which we call the Probabilistic Broad-Match
(PBM) mechanism. Different from SBM that puts together the ads bidding on all
the keywords matched to a given query for the GSP auction, the GSP with PBM
(denoted as PBM-GSP) randomly samples a keyword according to a predefined
probability distribution and only runs the GSP auction for the ads bidding on
this sampled keyword. We perform a comprehensive study on the theoretical
properties of the PBM-GSP. Specifically, we study its social welfare in the
worst equilibrium, in both full-information and Bayesian settings. The results
show that PBM-GSP can generate larger welfare than SBM-GSP under mild
conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in
Bayesian setting. To the best of our knowledge, this is the first work on
broad-match mechanisms for GSP that goes beyond the single-slot case and the
full-information setting