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On most sponsored search platforms, advertisers bid on some keywords fortheir advertisements (ads). Given a search request, ad retrieval modulerewrites the query into bidding keywords, and uses these keywords as keys toselect Top N ads through inverted indexes. In this way, an ad will not beretrieved even if queries are related when the advertiser does not bid oncorresponding keywords. Moreover, most ad retrieval approaches regard rewritingand ad-selecting as two separated tasks, and focus on boosting relevancebetween search queries and ads. Recently, in e-commerce sponsored search moreand more personalized information has been introduced, such as user profiles,long-time and real-time clicks. Personalized information makes ad retrievalable to employ more elements (e.g. real-time clicks) as search signals andretrieval keys, however it makes ad retrieval more difficult to measure adsretrieved through different signals. To address these problems, we propose anovel ad retrieval framework beyond keywords and relevance in e-commercesponsored search. Firstly, we employ historical ad click data to initialize ahierarchical network representing signals, keys and ads, in which personalizedinformation is introduced. Then we train a model on top of the hierarchicalnetwork by learning the weights of edges. Finally we select the best edgesaccording to the model, boosting RPM/CTR. Experimental results on oure-commerce platform demonstrate that our ad retrieval framework achieves goodperformance.

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search