Corpus ID: 13240553

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 M. 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… Expand
kaggle2.blob.core.windows.net
18 Citations
Highly Influential Citations
1
Background Citations
6
Methods Citations
9
Results Citations
1

18 Citations

Citation Type

Citation Type

Hybrid Models of Factorization Machines with Neural Networks and Their Ensembles for Click-through Rate Prediction
  • Hakan Toğuç, R. Kuzu
  • Computer Science
  • 2020 5th International Conference on Computer Science and Engineering (UBMK)
  • 2020
TLDR
The best ensemble model is implemented on a real digital campaign to show both prediction performance and the financial outcome of the CTR prediction comparatively, and is based on the serial ensem-bling of hybrid models via Gradient Boosting Algorithm. Expand
CLICK-THROUGH RATE PREDICTION – TOP-5 SOLUTION FOR THE AVAZU CONTEST
Ad click-through rates (CTR) plays an important role in online advertising industry. The ad clicks with big history can be easily predicted using machine learning methods. In case of small number of… Expand
A New Approach for Advertising CTR Prediction Based on Deep Neural Network via Attention Mechanism
TLDR
The method exploits dimension reduction based on decomposition, takes advantage of the attention mechanism in neural network modelling, and improves FM to make feature interactions contribute differently to the prediction. Expand
Field-aware Factorization Machines for CTR Prediction
TLDR
This paper establishes FFMs as an effective method for classifying large sparse data including those from CTR prediction, and proposes efficient implementations for training FFMs and comprehensively analyze FFMs. Expand
Research on CTR Prediction Based on Deep Learning
TLDR
The method exploits dimension reduction based on decomposition and combines the power of field-aware factorization machines and deep learning to portray the nonlinear associated relationship of data to solve the sparse feature learning problem. Expand
Improving Ad Click Prediction by Considering Non-displayed Events
TLDR
This paper proposes a novel framework for counterfactual CTR prediction by considering not only displayed events but also non-displayed events and compares this framework against state-of-the-art conventional CTR models and existingcounterfactual learning approaches. Expand
CTR Prediction Models Considering the Dynamics of User Interest
TLDR
A deep-based dynamic interest perception network (DIPN) that can trace both positive interest and negative interest is proposed and the experimental results demonstrate that compared with state-of-the-art models, DIPN achieves the highest prediction performance. Expand
A CTR prediction model based on user interest via attention mechanism
TLDR
A novel attentive deep interest-based network model called ADIN that captures the interest sequence in the interest extractor layer, and the auxiliary losses are employed to produce the interest state with the deep supervision. Expand
E-Commerce Item Recommendation Based on Field-aware Factorization Machine
TLDR
The team Random Walker's approach to this challenge, which won the 3rd place in the RecSys 2015 contest, consists of casting the top-N recommendation task into a binary classification problem and extracting original features from the raw data using field-aware factorization machines and gradient boosting decision tree. Expand
Attention-over-Attention Field-Aware Factorization Machine
TLDR
This paper proposes the field-aware embedding layer to exploit the field information of features, and combines it with the attention-over-attention mechanism to learn both feature-level and interaction-level attention to estimate the weight of feature interactions. Expand
...
1
2
...

References

SHOWING 1-10 OF 17 REFERENCES
Response prediction using collaborative filtering with hierarchies and side-information
TLDR
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. Expand
Collaborative Filtering Ensemble for Ranking
TLDR
The solution of the team "commendo" on the Track2 dataset of the KDD Cup 2011 Dror et al.. Expand
Predicting Ads’ Click-Through Rate with Decision Rules
Paid advertisements displayed alongside search results constitute a major source of income for search companies. Optimizations leading to more clicks on ads are a target goal shared by advertisers… Expand
Identification of factors predicting clickthrough in Web searching using neural network analysis
TLDR
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. Expand
Learning User Behaviors for Advertisements Click Prediction
Predicting potential advertisement clicks of users are important for advertisement recommendation, advertisement placement, presentation pricing, and so on. In this paper, several machine learning… Expand
Predicting click through rate for job listings
TLDR
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. Expand
Predicting clicks: estimating the click-through rate for new ads
TLDR
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. Expand
Improving regularized singular value decomposition for collaborative filtering
TLDR
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. Expand
Greedy function approximation: A gradient boosting machine.
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions… Expand
Optimising area under the ROC curve using gradient descent
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… Expand
...
1
2
...