Abstract
In the era of intelligent economy,the click-through rate(CTR)prediction system can evaluate massive service information based on user historical information,and screen out the products that are most likely to be favored by users,thus realizing customized push of information and achieve the ultimate goal of improving economic benefits.Sequence modeling is one of the main research directions of CTR prediction models based on deep learning.The user's general interest hidden in the entire click history and the short-term interest hid-den in the recent click behaviors have different influences on the CTR prediction results,which are highly important.In terms of capturing the user's general interest,existing models paid more attention to the relationships between item embedding vectors(point-level),while ig-noring the relationships between elements in item embedding vectors(union-level).The Lambda layer-based Convolutional Sequence Em-bedding(LCSE)model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution,and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level.In addition,we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction re-sults.The AUC(Area Under Curve)values of the LCSE model on the datasets Electronic,Movie & TV and MovieLens are 0.870 7,0.903 6 and 0.946 7,improving 0.45％,0.36％and 0.07％over the Caser model,proving the effectiveness of our proposed model.
基金项目
National Natural Science Foundation of China(62272214)
NETL
NSTL
万方数据