6 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
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
RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search (Student Abstract)
Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM). Existing sponsored search models are all based on traditional statistical models, which have poor RPM performance when queries follow a heavy-tailed distribution. Here, we propose an RPMoriented Query Rewriting Framework (RQRF) which outputs related bid keywords that can yield high RPM. RQRF embeds both queries and bid keywords to vectors in the same implicit space, converting the rewriting probability between each query and keyword to the distance between the two vectors. For label construction, we propose an RPM-oriented sample construction method, labeling keywords based on whether or not they can lead to high RPM. Extensive experiments are conducted to evaluate performance of RQRF. In a one month large-scale real-world traffic of e-commerce sponsored search system, the proposed model significantly outperforms traditional baseline