Details

Autor(en) / Beteiligte

• Li, Hao
• Li, Caimao
• Hou, Yuquan
• Lin, Hao
• Chen, Qiuhong
• Wang, Hongzhi
• Song, Xianhua
• Lu, Zeguang
• Wang, Yang
• Zhu, Guobin
• Han, Qilong

Titel

Factorization Machine Based on Bitwise Feature Importance for CTR Prediction

Ist Teil von

• Data Science, p.29-40

Ort / Verlag

Singapore: Springer Nature Singapore

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Click-through-rate (CTR) prediction is a crucial task in recommendation systems. The accuracy of CTR prediction is strongly influenced by the precise extraction of essential data and the modeling strategy chosen. The data of the CTR task are often very sparse, and Factorization Machines (FMs) are a class of general predictors working effectively with it. However, the performance of FMs can be limited by the fixed feature representation and the same weight of different features. In this work, we propose an improved Bitwise Feature Importance Factorization Machine (BFIFM) to improve the accuracy. The necessity of learning the degree of effect of the same feature under various situations is learned through the low-order intersection method, and the deep neural network (DNN) in our model is used in parallel to study high-order intersections. According to the final results obtained, the BFIFM model significantly outperforms other state-of-the-art models.