Model for click-through rate prediction based on sequence features

ZHU Sihan; PU Jian

doi:10.3969/j.issn.1000-5641.201921006

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Jul. 2020

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ZHU Sihan, PU Jian. Model for click-through rate prediction based on sequence features[J]. Journal of East China Normal University (Natural Sciences), 2020, (4): 134-146. doi: 10.3969/j.issn.1000-5641.201921006

Citation:

ZHU Sihan, PU Jian. Model for click-through rate prediction based on sequence features[J]. Journal of East China Normal University (Natural Sciences), 2020, (4): 134-146. doi: 10.3969/j.issn.1000-5641.201921006

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Model for click-through rate prediction based on sequence features

doi: 10.3969/j.issn.1000-5641.201921006

ZHU Sihan,
PU Jian^,

School of Computer Science and Technology, East China Normal University, Shanghai　200062, China

Received Date: 2019-08-01
Available Online: 2020-07-20
Publish Date: 2020-07-25

Abstract

Abstract

The click-through rate (CTR) prediction model is an important component of mainstream recommendation systems. The model assigns a score to recommended items according to the predicted CTR and generates an optimized scoring function which in-turn influences an item’s display strategy; this helps generate improved business conversion rates and a better user experience. Generally, CTR prediction models utilize both user and item features to predict CTR. However, structural characteristics of user behavior, such as frequency and trends, can also reflect behavioral tendencies. Given the absence of this information, this paper analyzes user behavior sequences as a time series and extracts latent features. Factorization machines are then used to learn from user/item features combined with sequence features to improve the quality of prediction. Experiments show that the sequence feature-based methods improve the performance of CTR prediction models and make CTR prediction more accurate.
- click-through rate (CTR) prediction,
- recommendation system,
- auto-regressive moving average model（ARMA）,
- factorization machine

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References(19)

References

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