Semantic Keywords Clustering to Optimize Text Ads Campaigns

In this paper, we describe how to use some well-known machine learning tools to make groups of textual queries of similar semantic meaning. Such a clusterization can be used to improve the performances of bidding algorithms for online advertising, by mutualizing the signal gathered by text ads displayed on result pages of search queries which share a similar meaning. Indeed, search engines organize auctions wherein participants bid on selected search terms on which they wish to display an ad. Generalist e-commerce companies such as Cdiscount bid simultaneously on millions of terms that reflect the diversity of their catalog of products, according to the expected profits associated with the ads. Methods to estimate these expected returns suffer from a sparsity of data, since most of the keywords have little or no historical signal. Grouping them and exploiting information on the most frequent keywords (short tail) to infer information on the less frequent ones (long tail), allow to anticipate the user behavior by semantics and improve the bidding strategy. The plan is the following: pre-process the keywords by stemming, choose an e-commerce training corpus for the Word2Vec model, train it, and perform an embedding into a euclidean space where we can cluster keywords thanks to a K-means algorithm. We validate our approach on a sub-sample of the keywords for which they have a non-semantic distance available. Finally, all the keywords in the same cluster share the same bid, which is computed aggregating the cluster historical signal.