Biblio

Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.

Integrity constraints, guiding the cleaning of dirty data, are often found to be imprecise as well. Existing studies consider the inaccurate constraints that are oversimplified, and thus refine the constraints via inserting more predicates (attributes). We note that imprecise constraints may not only be oversimplified so that correct data are erroneously identified as violations, but also could be overrefined that the constraints overfit the data and fail to identify true violations. In the latter case, deleting excessive predicates applies. To address the oversimplified and overrefined constraint inaccuracies, in this paper, we propose to repair data by allowing a small variation (with both predicate insertion and deletion) on the constraints. A novel θ-tolerant repair model is introduced, which returns a (minimum) data repair that satisfies at least one variant of the constraints (with constraint variation no greater than θ compared to the given constraints). To efficiently repair data among various constraint variants, we propose a single round, sharing enabled approach. Results on real data sets demonstrate that our proposal can capture more accurate data repairs compared to the existing methods with/without constraint repairs.