Clicks prediction with L1 regularized logistic regression and a study on poisson factorization recommender evaluation
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The key task for a search engine advertising system is, for each query that the search engine receives, to choose what advertisement should be displayed, and in what order. This ranking order has a strong impact on the revenue the search engine receives from the ads. Meanwhile, showing the user an advertisement that they prefer to click on improves user satisfaction. Therefore, it is reasonable to set up click-through rate (CTR) as a weighted ranking criteria. In this project, we aim to develop a model capable of accurately predicting CTR of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. Combining logistic regression model with L1 regularization we are able to predict CTR for new ads based on self-defined features. The ultimate goal is to improve the convergence and performance of our advertising system, consequently increasing both revenue and user satisfaction. Second part of this report is about a study on poisson factorization recommender evaluation. As recommender systems become more and more popular, many approaches have been suggested to evaluate the performance of traditional recommenders based on Gaussian distribution. However, few evaluating approaches were designed for poisson factorization recommender. This study checked some most common evaluation methods and discussed about their appropriateness in poisson factorization recommender evaluation.