6,373 research outputs found
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative
Accurately predicting conversions in advertisements is generally a
challenging task, because such conversions do not occur frequently. In this
paper, we propose a new framework to support creating high-performing ad
creatives, including the accurate prediction of ad creative text conversions
before delivering to the consumer. The proposed framework includes three key
ideas: multi-task learning, conditional attention, and attention highlighting.
Multi-task learning is an idea for improving the prediction accuracy of
conversion, which predicts clicks and conversions simultaneously, to solve the
difficulty of data imbalance. Furthermore, conditional attention focuses
attention of each ad creative with the consideration of its genre and target
gender, thus improving conversion prediction accuracy. Attention highlighting
visualizes important words and/or phrases based on conditional attention. We
evaluated the proposed framework with actual delivery history data (14,000
creatives displayed more than a certain number of times from Gunosy Inc.), and
confirmed that these ideas improve the prediction performance of conversions,
and visualize noteworthy words according to the creatives' attributes.Comment: 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 2019) as an applied data science
pape
Ensembled CTR Prediction via Knowledge Distillation
Recently, deep learning-based models have been widely studied for
click-through rate (CTR) prediction and lead to improved prediction accuracy in
many industrial applications. However, current research focuses primarily on
building complex network architectures to better capture sophisticated feature
interactions and dynamic user behaviors. The increased model complexity may
slow down online inference and hinder its adoption in real-time applications.
Instead, our work targets at a new model training strategy based on knowledge
distillation (KD). KD is a teacher-student learning framework to transfer
knowledge learned from a teacher model to a student model. The KD strategy not
only allows us to simplify the student model as a vanilla DNN model but also
achieves significant accuracy improvements over the state-of-the-art teacher
models. The benefits thus motivate us to further explore the use of a powerful
ensemble of teachers for more accurate student model training. We also propose
some novel techniques to facilitate ensembled CTR prediction, including teacher
gating and early stopping by distillation loss. We conduct comprehensive
experiments against 12 existing models and across three industrial datasets.
Both offline and online A/B testing results show the effectiveness of our
KD-based training strategy.Comment: Published in CIKM'202
Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey
Multi-task learning has been widely applied in computational vision, natural
language processing and other fields, which has achieved well performance. In
recent years, a lot of work about multi-task learning recommender system has
been yielded, but there is no previous literature to summarize these works. To
bridge this gap, we provide a systematic literature survey about multi-task
recommender systems, aiming to help researchers and practitioners quickly
understand the current progress in this direction. In this survey, we first
introduce the background and the motivation of the multi-task learning-based
recommender systems. Then we provide a taxonomy of multi-task learning-based
recommendation methods according to the different stages of multi-task learning
techniques, which including task relationship discovery, model architecture and
optimization strategy. Finally, we raise discussions on the application and
promising future directions in this area
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