ABSTRACT
Recently there has been a surge in research that predicts retrieval relevance using historical click-through data. While a larger number of clicks between a query and a document provides a stronger ``confidence" of relevance, most models in the literature that learn from clicks are error-prone as they do not take into account any confidence estimates. Sponsored Search models are especially prone to this error as they are typically trained on search engine logs in order to predict click-through-rate (CTR). The estimated CTR ultimately determines the rank at which an ad is shown and also impacts the price (cost-per-click) for the advertiser. In this paper, we improve a model that applies collaborative filtering on click data by training a filter that has been trained to predict pure relevance. Applying the filter to ads that have seen few clicks on live traffic results in improved CTR and click-yield (CY). Additionally, in offline experiments we find that using features based on the \emph{organic} results improves the relevance based filter's performance.
- J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of UAI '98 Uncertainty in Artificial Intelligence, July 1998. Google ScholarDigital Library
- N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR '07, 2007. Google ScholarDigital Library
- B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review, 97(1), March 2007.Google ScholarCross Ref
- H. Raghavan and R. Iyer. Evaluating vector-space and probabilistic models for query to ad matching. In SIGIR '08 Workshop on Information Retrieval in Advertising (IRA), 2008.Google Scholar
- M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In WWW '07, 2007. Google ScholarDigital Library
Index Terms
- A relevance model based filter for improving ad quality
Recommendations
Improving ad relevance in sponsored search
WSDM '10: Proceedings of the third ACM international conference on Web search and data miningWe describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel ...
Improving Search Effectiveness with Field-based Relevance Modeling
ADCS '18: Proceedings of the 23rd Australasian Document Computing SymposiumFields are a valuable auxiliary source of information in semi-structured HTML web documents. So, it is no surprise that ranking models have been designed to leverage this information to improve search effectiveness. We present the first (initial) study ...
Advertising keyword suggestion based on concept hierarchy
WSDM '08: Proceedings of the 2008 International Conference on Web Search and Data MiningThe increasing growth of the World Wide Web constantly enlarges the revenue generated by search engine advertising. Advertisers bid on keywords associated with their products to display their ads on the search result pages. Keyword suggestion methods ...
Comments