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
Click-through information is considered as a valuable source of users＇ implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputati...Click-through information is considered as a valuable source of users＇ implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputation, researchers have proposed a variety of assumptions to generate a reasonable estimation of result relevance. Therefore, many click models have been proposed to describe how user click action happens and to predict click probability （and search result relevance）. This work builds upon many years of existing efforts from THUIR labs, summarizes the most recent advances and provides a series of practical click models. In this paper, we give an introduction of how to build an effective click model. We use two click models as specific examples to introduce the general procedures of building a click model. We also introduce common evaluation metrics for the comparison of different click models. Some useful datasets and tools are also introduced to help readers better understand and implement existing click models. The goal of this survey is to bring together current efforts in the area, summarize the research performed so far and give a view on building click models for web search.展开更多
In this paper, first studied are the distribution characteristics of user behaviors based on log data from a massive web search engine. Analysis shows that stochastic distribution of user queries accords with the char...In this paper, first studied are the distribution characteristics of user behaviors based on log data from a massive web search engine. Analysis shows that stochastic distribution of user queries accords with the characteristics of power-law function and exhibits strong similarity, and the user' s queries and clicked URLs present dramatic locality, which implies that query cache and 'hot click' cache can be employed to improve system performance. Then three typical cache replacement policies are compared, including LRU, FIFO, and LFU with attenuation. In addition, the distribution character-istics of web information are also analyzed, which demonstrates that the link popularity and replica pop-ularity of a URL have positive influence on its importance. Finally, variance between the link popularity and user popularity, and variance between replica popularity and user popularity are analyzed, which give us some important insight that helps us improve the ranking algorithms in a search engine.展开更多
In taking into full consideration of the technology acceptance model(TAM),this empirical study has made a few assumptions and also has formulated a model for the study on the level of satisfaction of database users. T...In taking into full consideration of the technology acceptance model(TAM),this empirical study has made a few assumptions and also has formulated a model for the study on the level of satisfaction of database users. This research project was conducted by collecting relevant data from library users of five universities. Specifically, it aimed to measure database users' level of satisfaction and tried to find factors affecting these graduate students who are using databases regularly at their university libraries. An analysis of the collected data shows that the level of database users' satisfaction could be directly affected by the database service quality, the easiness of accessing the system and user perceived notion of usefulness of those databases that they use often. This study also found that database users' gender could be a significant factor in their perceived notion of easiness of accessing databases, and also in their perceived notion of satisfaction for their successful information retrieval operations. The frequency of accessing databases by these graduate students has an impact on users' perceived notion of easiness of database access. The users' school classifications could make a significant difference in their perceived notion on the extent of usefulness of a particular database.展开更多
Most of the behavior models with respect to Web applications focus on sequencing of events,without regard for the changes of parameters or elements and the relationship between trigger conditions of events and Web pag...Most of the behavior models with respect to Web applications focus on sequencing of events,without regard for the changes of parameters or elements and the relationship between trigger conditions of events and Web pages.As a result,these models are not sufficient to effectively represent the dynamic behavior of the Web2.0 application.Therefore,in this paper,to appropriately describe the dynamic behavior of the client side of Web applications,we define a novel Client-side Behavior Model(CBM)for Web applications and present a user behavior trace-based modeling method to automatically generate and optimize CBMs.To verify the effectiveness of our method,we conduct a series of experiments on six Web applications according to three types of user behavior traces.The experimental results show that our modeling method can construct CBMs automatically and effectively,and the CBMs built are more precise to represent the dynamic behavior of Web applications.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Click-through information is considered as a valuable source of users＇ implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputation, researchers have proposed a variety of assumptions to generate a reasonable estimation of result relevance. Therefore, many click models have been proposed to describe how user click action happens and to predict click probability （and search result relevance）. This work builds upon many years of existing efforts from THUIR labs, summarizes the most recent advances and provides a series of practical click models. In this paper, we give an introduction of how to build an effective click model. We use two click models as specific examples to introduce the general procedures of building a click model. We also introduce common evaluation metrics for the comparison of different click models. Some useful datasets and tools are also introduced to help readers better understand and implement existing click models. The goal of this survey is to bring together current efforts in the area, summarize the research performed so far and give a view on building click models for web search.
基金This work was supported by the National Grand Fundamental Research of China ( Grant No. G1999032706).
文摘In this paper, first studied are the distribution characteristics of user behaviors based on log data from a massive web search engine. Analysis shows that stochastic distribution of user queries accords with the characteristics of power-law function and exhibits strong similarity, and the user' s queries and clicked URLs present dramatic locality, which implies that query cache and 'hot click' cache can be employed to improve system performance. Then three typical cache replacement policies are compared, including LRU, FIFO, and LFU with attenuation. In addition, the distribution character-istics of web information are also analyzed, which demonstrates that the link popularity and replica pop-ularity of a URL have positive influence on its importance. Finally, variance between the link popularity and user popularity, and variance between replica popularity and user popularity are analyzed, which give us some important insight that helps us improve the ranking algorithms in a search engine.
基金supported by the Ministry of Education of China(Grant No.05JZD00024)
文摘In taking into full consideration of the technology acceptance model(TAM),this empirical study has made a few assumptions and also has formulated a model for the study on the level of satisfaction of database users. This research project was conducted by collecting relevant data from library users of five universities. Specifically, it aimed to measure database users' level of satisfaction and tried to find factors affecting these graduate students who are using databases regularly at their university libraries. An analysis of the collected data shows that the level of database users' satisfaction could be directly affected by the database service quality, the easiness of accessing the system and user perceived notion of usefulness of those databases that they use often. This study also found that database users' gender could be a significant factor in their perceived notion of easiness of accessing databases, and also in their perceived notion of satisfaction for their successful information retrieval operations. The frequency of accessing databases by these graduate students has an impact on users' perceived notion of easiness of database access. The users' school classifications could make a significant difference in their perceived notion on the extent of usefulness of a particular database.
基金supported by the National Natural Science Foundation of China(Nos.61672085,61702029,and 61872026)。
文摘Most of the behavior models with respect to Web applications focus on sequencing of events,without regard for the changes of parameters or elements and the relationship between trigger conditions of events and Web pages.As a result,these models are not sufficient to effectively represent the dynamic behavior of the Web2.0 application.Therefore,in this paper,to appropriately describe the dynamic behavior of the client side of Web applications,we define a novel Client-side Behavior Model(CBM)for Web applications and present a user behavior trace-based modeling method to automatically generate and optimize CBMs.To verify the effectiveness of our method,we conduct a series of experiments on six Web applications according to three types of user behavior traces.The experimental results show that our modeling method can construct CBMs automatically and effectively,and the CBMs built are more precise to represent the dynamic behavior of Web applications.
