摘要
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.
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
This work was supported by the National Grand Fundamental Research of China ( Grant No. G1999032706).
