29,932 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
Estimating Position Bias without Intrusive Interventions
Presentation bias is one of the key challenges when learning from implicit
feedback in search engines, as it confounds the relevance signal. While it was
recently shown how counterfactual learning-to-rank (LTR) approaches
\cite{Joachims/etal/17a} can provably overcome presentation bias when
observation propensities are known, it remains to show how to effectively
estimate these propensities. In this paper, we propose the first method for
producing consistent propensity estimates without manual relevance judgments,
disruptive interventions, or restrictive relevance modeling assumptions. First,
we show how to harvest a specific type of intervention data from historic
feedback logs of multiple different ranking functions, and show that this data
is sufficient for consistent propensity estimation in the position-based model.
Second, we propose a new extremum estimator that makes effective use of this
data. In an empirical evaluation, we find that the new estimator provides
superior propensity estimates in two real-world systems -- Arxiv Full-text
Search and Google Drive Search. Beyond these two points, we find that the
method is robust to a wide range of settings in simulation studies
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