Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction
@inproceedings{Tscher2012EnsembleOC, title={Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction}, author={Andreas T{\"o}scher and Michael Jahrer and Jeong-Yoon Lee}, year={2012} }
The challenge for Track 2 of the KDD Cup 2012 competition was to predict the click-through rate (CTR) of web advertisements given information about the ad, the query and the user. Our solution comprised an ensemble of models, combined using an artificial neural network. We built collaborative filters, probability models, and feature engineered models to predict CTRs. In addition, we developed a few models which directly optimized AUC, including the collaborative filters and ANN models. These…
19 Citations
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References
SHOWING 1-10 OF 17 REFERENCES
Response prediction using collaborative filtering with hierarchies and side-information
- Computer ScienceKDD
- 2011
This paper shows how response prediction can be viewed as a problem of matrix completion, and proposes to solve it using matrix factorization techniques from collaborative filtering (CF), and shows how this factorization can be seamlessly combined with explicit features or side-information for pages and ads, which let us combine the benefits of both approaches.
Collaborative Filtering Ensemble for Ranking
- Computer ScienceKDD Cup
- 2012
The solution of the team "commendo" on the Track2 dataset of the KDD Cup 2011 Dror et al..
Predicting Ads’ Click-Through Rate with Decision Rules
- Computer Science
- 2008
This paper builds upon a large data set with real ad clicks and impressions acquired thanks to Microsoft’s Beyond Search program, and presents an algorithm learning an ensemble of decision rules that can be used for predicting the CTR for unseen ads and giving recommendations to improve ads’ quality.
Learning User Behaviors for Advertisements Click Prediction
- Computer Science
- 2011
Several machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), decision tree (DT) and backpropagation neural networks (BPN) are developed to learn user's click behaviors from advertisement search and click logs to study the impact of feature selection algorithms on the prediction models.
Identification of factors predicting clickthrough in Web searching using neural network analysis
- Computer Science, BusinessJ. Assoc. Inf. Sci. Technol.
- 2009
This research uses a neural network to detect the significant influence of searching characteristics on future user clickthrough, and shows that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough.
Predicting click through rate for job listings
- BusinessWWW '09
- 2009
This work learns regression models using features of the job, optional click history of job, features of "related" jobs, and shows that their models predict CTR much better than predicting avg.
Predicting clicks: estimating the click-through rate for new ads
- Computer ScienceWWW '07
- 2007
This work shows that it can be used to use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads, and shows that using this model improves the convergence and performance of an advertising system.
Improving regularized singular value decomposition for collaborative filtering
- Computer Science
- 2007
Different efficient collaborative filtering techniques and a framework for combining them to obtain a good prediction are described, predicting users’ preferences for movies with error rate 7.04% better on the Netflix Prize dataset than the reference algorithm Netflix Cinematch.
Greedy function approximation: A gradient boosting machine.
- Computer Science
- 2001
A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Optimising area under the ROC curve using gradient descent
- Computer ScienceICML
- 2004
This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective…