Journal of East China Normal University(Natural Science) ›› 2020, Vol. 2020 ›› Issue (4): 134-146.doi: 10.3969/j.issn.1000-5641.201921006

• Computer Science • Previous Articles     Next Articles

Model for click-through rate prediction based on sequence features

ZHU Sihan, PU Jian   

  1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
  • Received:2019-08-01 Published:2020-07-20

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.

Key words: click-through rate (CTR) prediction, recommendation system, auto-regressive moving average model(ARMA), factorization machine

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