Implementation of personalized search in active web intermediaries

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Personalized information service is an active area in many research communities. In the web search area, the personalization focuses on how to adapt the search result according to user's content-oriented preferences. In the content delivery area, the personalization focuses on how to adapt the web page according to users' performance-oriented preferences. If the personalization could be implemented considering both content and performance, it would be really beneficial to the end user. It is the purpose of this work to leverage the advances in both areas to implement the search personalization. Our objective is to implement the personalized search service in web intermediaries based on users' requirements on content relevancy as well as on quality of service. Currently, the intermediary mainly refers to the proxy server or edge server. In the long term, the concept of the intermediary could be generalized to any information service agent sitting between clients and servers, and the search target is not limited to the web page, it could also be a web service. There are several major steps involved: 1) to define quality of service factors which are closely related to the web search; 2) to design and implement a content adaptation framework for the personalized search; 3) to design and implement a re-ranking algorithm which could integrate the performance-related quality of service factors (e.g. latency of fetching a page, user's connection speed) into the traditional information retrieval model. The key features of this proposal would be: 1) search personalization is implemented as a content adaptation service in a web intermediary, which could fully utilize the rich user data saved in its logs and its information concerning the page delivery performance and the client capabilities; 2) the proposed re-ranking algorithm could augment the existing ranking models, which is especially useful in a ubiquitous computing environment; 3) we will train the highly qualified personnel to gain a deep understanding on web search, adaptive content delivery, and personalization. They will have concrete experiences on system building and systematic experimental methods, which would benefit their future career.