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Multi-View Multi-Task Campaign Embedding for Cold-Start Conversion Rate Forecasting
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AI
DOI：10.1109/TBDATA.2022.3162150.png)
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In online advertising, it is critical for advertisers to forecast conversion rate (CVR) of campaigns. Previous work on campaign forecasting concentrates on the time-series analysis which depend on the availability of a length of history. However, these approaches become inadequate for cold-start campaigns which lack for the observation of past. In this work, we attempt to mitigate this challenge by learning an unsupervised and composite campaign embedding to capture multi-view semantic relationships on campaign information, and consequently forecasting the cold-start campaigns using the nearest neighbor campaigns. Specifically, we propose a novel embedding framework which simultaneously extracts and fuses heterogeneous knowledge from multiple views of campaign data in a multi-task learning fashion, to learn the semantic relationship of ad message, conversion rule, and audience targeting. We develop a hierarchical attention mechanism to refine the embedding model at two levels - an intra-view attention to improve context aggregation, and an inter-task attention to balance task importance. Finally, we adopt the k-NN regression model to predict the CVR based on the neighboring campaigns in the embedding space which encodes the multi-view campaign proximity. We conduct extensive experiments on a real-world advertising campaign dataset. The results demonstrate the effectiveness of the proposed embedding method for CVR forecasting in cold-start scenarios.
Keyword:
Forecasting
Semantics
Task analysis
Big Data
Fuses
Multitasking
Advertising
Online advertising
cold-start forecasting
campaign embedding
multi-view learning
期刊
I
IF:
5.7
论文数:
860
被引数:
3.0K
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