Advertising keyword generation using active learning

Wu, Hao; Qiu, Guang; He, Xiaofei; Shi, Yuan; Qu, Mingcheng; Shen, Jiemin; Bu, Jiajun; Chen, Chun

doi:10.1145/1526709.1526873

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…In the branch of query log-based methods, keywords are mainly suggested by conducting association/co-occurrence analysis in search engine query logs [34,68,70]. Proximity-based keyword generation methods query search engines with the seed keyword and recommend keywords from the query results possessing high proximity to the seed keyword [1,58]. In addition, some efforts calculate the proximity based on vocabulary dictionaries/corpus pre-constructed by domain experts [11], e.g., thesaurus dictionary, Wikipedia, etc.…”

Section: Keyword Decisionsmentioning

confidence: 99%

Optimal Keywords Grouping in Sponsored Search Advertising Under Uncertain Environments

Yang

2020

International Journal of Electronic Commerce

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In sponsored search advertising, advertisers need to make a series of keyword decisions. Among them, how to group these keywords to form several adgroups within a campaign is a challenging task, due to the highly uncertain environment of search advertising. This paper proposes a stochastic programming model for keywords grouping, taking click-through rate and conversion rate as random variables, with consideration of budget constraints and advertisers' risktolerance. A branch-and-bound algorithm is developed to solve our model. Furthermore, we conduct computational experiments to evaluate the effectiveness of our model and solution, with two real-world datasets collected from reports and logs of search advertising campaigns. Experimental results illustrated that our keywords grouping approach outperforms five baselines, and it can approximately approach the optimum in a steady way. This research generates several interesting findings that illuminate critical managerial insights for advertisers in sponsored search advertising. First, keywords grouping does matter for advertisers, especially in the situation with a large number of keywords. Second, in keyword grouping decisions, the marginal profit does not necessarily show the marginal diminishing phenomenon as the budget increases. Such that, it's a worthy try for advertisers to increase their budget in keywords grouping decisions, in order to obtain additional profit. Third, the optimal keywords grouping solution is a result of multifaceted trade-off among various advertising factors. In particular, assigning more keywords into adgroups or having more budget won't certainly lead to higher profits. This suggests a warning for advertisers that it's not wise to take the number of keywords as the criterion for keywords grouping decisions.

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Section: Keyword Decisionsmentioning

confidence: 99%

Optimal Keywords Grouping in Sponsored Search Advertising Under Uncertain Environments

Yang

2020

International Journal of Electronic Commerce

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“…In academia, SS has also attracted a lot of attention from researchers in related fields. Typical research problems for SS include bidding optimization [2,4,8,28], click prediction [1,9,12,24], keyword suggestion [20,25], auction mechanism design [10], etc. However, the majority of existing works were focused on the platform side or the advertiser side.…”

Section: Related Workmentioning

confidence: 99%

Personalized Attraction Enhanced Sponsored Search with Multi-task Learning

Zhao

Zhang

Wang³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of products that attract them. This not only benefits merchants and the platform, but also improves user experience. The problem is challenging due to the following reasons: (1) We need to carefully manipulate the ad content without affecting user search experience. (2) It is difficult to obtain users' explicit feedback of their preference in product features.(3) Nowadays, a great portion of the search traffic in E-Commerce platforms is from their mobile apps (e.g., nearly 90% in Taobao). The situation would get worse in the mobile setting due to limited space. We are focused on the mobile setting and propose to manipulate ad titles by adding a few selling point keywords (SPs) to attract query users. We model it as a personalized attractive SP prediction problem and carry out both large-scale offline evaluation and online A/B tests in Taobao. The contributions include: (1) We explore various exhibition schemes of SPs. (2) We propose a surrogate of user explicit feedback for SP preference. (3) We also explore multi-task learning and various additional features to boost the performance. A variant of our best model has already been deployed in Taobao, leading to a 2% increase in revenue per thousand impressions and an opt-out rate of merchants less than 4%.

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“…Active learning was used for generating non-intuitive keywords that do not contain the original seed term (Wu et al, 2009). A large set of candidate terms (those with the highest TF-IDF scores) were extracted from the search engine results to the seed term.…”

Section: How Can Alice Use Acme's Marketing Budget Sparingly?mentioning

confidence: 99%

Lean Marketing: Know who not to advertise to!

Bulut

2015

Electronic Commerce Research and Applications

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Advertising keyword generation using active learning

Cited by 14 publications

References 4 publications

Optimal Keywords Grouping in Sponsored Search Advertising Under Uncertain Environments

Optimal Keywords Grouping in Sponsored Search Advertising Under Uncertain Environments

Personalized Attraction Enhanced Sponsored Search with Multi-task Learning

Lean Marketing: Know who not to advertise to!

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