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Online Interaction Detection for Click-Through Rate Prediction

arXiv 2106.15400 ↗First posted 27 Jun 2021cs.LG stat.MLcited by 0

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Abstract (arXiv)

Click-Through Rate prediction aims to predict the ratio of clicks to impressions of a specific link. This is a challenging task since (1) there are usually categorical features, and the inputs will be extremely high-dimensional if one-hot encoding is applied, (2) not only the original features but also their interactions are important, (3) an effective prediction may rely on different features and interactions in different time periods. To overcome these difficulties, we propose a new interaction detection method, named Online Random Intersection Chains. The method, which is based on the idea of frequent itemset mining, detects informative interactions by observing the intersections of randomly chosen samples. The discovered interactions enjoy high interpretability as they can be comprehended as logical expressions. ORIC can be updated every time new data is collected, without being retrained on historical data. What's more, the importance of the historical and latest data can be controlled by a tuning parameter. A framework is designed to deal with the streaming interactions, so almost all existing models for CTR prediction can be applied after interaction detection. Empirical results demonstrate the efficiency and effectiveness of ORIC on three benchmark datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 48 references that are in the map. First posted on arXiv 27 Jun 2021.

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30 of 48 references.

References · 30

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18 more
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