Modem search engines record user interactions and use them to improve search quality. In particular, user click-through has been successfully used to improve click- through rate （CTR）, Web search ranking, and query ...Modem search engines record user interactions and use them to improve search quality. In particular, user click-through has been successfully used to improve click- through rate （CTR）, Web search ranking, and query rec- ommendations and suggestions. Although click-through logs can provide implicit feedback of users＇ click preferences, de- riving accurate absolute relevance judgments is difficult be- cause of the existence of click noises and behavior biases. Previous studies showed that user clicking behaviors are bi- ased toward many aspects such as ＂position＂ （user＇s attention decreases from top to bottom） and ＂trust＂ （Web site reputa- tions will affect user＇s judgment）. To address these problems, researchers have proposed several behavior models （usually referred to as click models） to describe users？ practical browsing behaviors and to obtain an unbiased estimation of result relevance. In this study, we review recent efforts to construct click models for better search ranking and propose a novel convolutional neural network architecture for build- ing click models. Compared to traditional click models, our model not only considers user behavior assumptions as input signals but also uses the content and context information of search engine result pages. In addition, our model uses pa- rameters from traditional click models to restrict the meaning of some outputs in our model＇s hidden layer. Experimental results show that the proposed model can achieve consider- able improvement over state-of-the-art click models based on the evaluation metric of click perplexity.展开更多
In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,t...In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.展开更多
文摘Modem search engines record user interactions and use them to improve search quality. In particular, user click-through has been successfully used to improve click- through rate （CTR）, Web search ranking, and query rec- ommendations and suggestions. Although click-through logs can provide implicit feedback of users＇ click preferences, de- riving accurate absolute relevance judgments is difficult be- cause of the existence of click noises and behavior biases. Previous studies showed that user clicking behaviors are bi- ased toward many aspects such as ＂position＂ （user＇s attention decreases from top to bottom） and ＂trust＂ （Web site reputa- tions will affect user＇s judgment）. To address these problems, researchers have proposed several behavior models （usually referred to as click models） to describe users？ practical browsing behaviors and to obtain an unbiased estimation of result relevance. In this study, we review recent efforts to construct click models for better search ranking and propose a novel convolutional neural network architecture for build- ing click models. Compared to traditional click models, our model not only considers user behavior assumptions as input signals but also uses the content and context information of search engine result pages. In addition, our model uses pa- rameters from traditional click models to restrict the meaning of some outputs in our model＇s hidden layer. Experimental results show that the proposed model can achieve consider- able improvement over state-of-the-art click models based on the evaluation metric of click perplexity.
文摘In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.
