Biblio

For the purpose of outdoor advertising market researching, AD images are recaptured and uploaded everyday for statistics. But the quality of the recaptured advertising images are often affected by conditions such as angle, distance, and light during the shooting process, which consequently reduce either the speed or the accuracy of the retrieving algorithm. In this paper, we proposed a hash retrieval method based on convolutional neural networks for recaptured images. The basic idea is to add a hash layer to the convolutional neural network and then extract the binary hash code output by the hash layer to perform image retrieval in lowdimensional Hamming space. Experimental results show that the retrieval performance is improved compared with the current commonly used hash retrieval methods.

The online advertising ecosystem is built upon the massive data collection of ad networks to learn the properties of users for targeted ad deliveries. Existing efforts have investigated the privacy leakage behaviors of mobile ad networks. However, there lacks a large-scale measurement study to evaluate the scale of privacy leakage through mobile ads. In this work, we present a study of the potential privacy leakage in location-based mobile advertising services based on a large-scale measurement. We first introduce a threat model in the mobile ad ecosystem, and then design a measurement system to perform extensive threat measurements and assessments. To counteract the privacy leakage threats, we design and implement an adaptive location obfuscation mechanism, which can be used to obfuscate location data in real-time while minimizing the impact to mobile ad businesses.

The online advertising ecosystem is built upon the massive data collection of ad networks to learn the properties of users for targeted ad deliveries. Existing efforts have investigated the privacy leakage behaviors of mobile ad networks. However, there lacks a large-scale measurement study to evaluate the scale of privacy leakage through mobile ads. In this work, we present a study of the potential privacy leakage in location-based mobile advertising services based on a large-scale measurement. We first introduce a threat model in the mobile ad ecosystem, and then design a measurement system to perform extensive threat measurements and assessments. To counteract the privacy leakage threats, we design and implement an adaptive location obfuscation mechanism, which can be used to obfuscate location data in real-time while minimizing the impact to mobile ad businesses.

Nowadays, a typical household owns multiple digital devices that can be connected to the Internet. Advertising companies always want to seamlessly reach consumers behind devices instead of the device itself. However, the identity of consumers becomes fragmented as they switch from one device to another. A naive attempt is to use deterministic features such as user name, telephone number and email address. However consumers might refrain from giving away their personal information because of privacy and security reasons. The challenge in ICDM2015 contest is to develop an accurate probabilistic model for predicting cross-device consumer identity without using the deterministic user information. In this paper we present an accurate and scalable cross-device solution using an ensemble of Gradient Boosting Decision Trees (GBDT) and Random Forest. Our final solution ranks 9th both on the public and private LB with F0.5 score of 0.855.

Multi-touch attribution, which allows distributing the credit to all related advertisements based on their corresponding contributions, has recently become an important research topic in digital advertising. Traditionally, rule-based attribution models have been used in practice. The drawback of such rule-based models lies in the fact that the rules are not derived form the data but only based on simple intuition. With the ever enhanced capability to tracking advertisement and users' interaction with the advertisement, data-driven multi-touch attribution models, which attempt to infer the contribution from user interaction data, become an important research direction. We here propose a new data-driven attribution model based on survival theory. By adopting a probabilistic framework, one key advantage of the proposed model is that it is able to remove the presentation biases inherit to most of the other attribution models. In addition to model the attribution, the proposed model is also able to predict user's 'conversion' probability. We validate the proposed method with a real-world data set obtained from a operational commercial advertising monitoring company. Experiment results have shown that the proposed method is quite promising in both conversion prediction and attribution.