Aardvark, which powers vark.com, published an interesting paper entitled "Anatomy of a Large-Scale Social Search Engine" at WWW 2010.
The paper talks about the social search engine applied in vark.com. The search engine is based on social graphs and topics instead of keyword. The paper addresses search engine like Google as library paradigm and Aardvark as village paradigm. The search engine of village paradigm gets answers by asking the one who are expert in the underlying topic in social graphs. In library paradigm, the search engine needs to figure out what a user what based on keywords, search history and user profile, which considers to be a very difficult task. The village paradigm leaves the difficult part to human being, so, only problem is to find the right person.
The model of Aardvark considers that a user u1 asks a question q, and the search engine should find the right user u2 to provide the answer. Aardvark associates both users and questions to topics. Aardvark extracts and stores a set of topics for every users. When a question q of user u1 comes, Aardvark extracts topics t from the question, and find the best user u2 in u1's social graph according to topics t. Aardvark need not to index all the questions/answers/articles on the web, but only topics and social graphs of users. The topics may be considered as the relationship between text and people, and the social graphs are the relationship between people.
The paper talks about the social search engine applied in vark.com. The search engine is based on social graphs and topics instead of keyword. The paper addresses search engine like Google as library paradigm and Aardvark as village paradigm. The search engine of village paradigm gets answers by asking the one who are expert in the underlying topic in social graphs. In library paradigm, the search engine needs to figure out what a user what based on keywords, search history and user profile, which considers to be a very difficult task. The village paradigm leaves the difficult part to human being, so, only problem is to find the right person.
The model of Aardvark considers that a user u1 asks a question q, and the search engine should find the right user u2 to provide the answer. Aardvark associates both users and questions to topics. Aardvark extracts and stores a set of topics for every users. When a question q of user u1 comes, Aardvark extracts topics t from the question, and find the best user u2 in u1's social graph according to topics t. Aardvark need not to index all the questions/answers/articles on the web, but only topics and social graphs of users. The topics may be considered as the relationship between text and people, and the social graphs are the relationship between people.
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