Relevance Ranking Using Terms (Cont.) Most systems add to the above model Words that occur in title,author list,section headings,etc.are given greater importance Words whose first occurrence is late in the document are given lower importance Very common words such as“a”,“an",“the",“it"etc are eliminated Called stop words Proximity:if keywords in query occur close together in the document,the document has higher importance than if they occur far apart Documents are returned in decreasing order of relevance score Usually only top few documents are returned,not all Database System Concepts-5th Edition,Sep 2,2005 19.7 ©Silberschat乜，Korth and Sudarshan
Database System Concepts - 5 19.7 ©Silberschatz, Korth and Sudarshan th Edition, Sep 2, 2005 Relevance Ranking Using Terms (Cont.) Most systems add to the above model Words that occur in title, author list, section headings, etc. are given greater importance Words whose first occurrence is late in the document are given lower importance Very common words such as “a”, “an”, “the”, “it” etc are eliminated Called stop words Proximity: if keywords in query occur close together in the document, the document has higher importance than if they occur far apart Documents are returned in decreasing order of relevance score Usually only top few documents are returned, not all
Similarity Based Retrieval Similarity based retrieval-retrieve documents similar to a given document Similarity may be defined on the basis of common words E.g.find k terms in Awith highest TF(d,t)/n(t and use these terms to find relevance of other documents Relevance feedback:Similarity can be used to refine answer set to keyword query User selects a few relevant documents from those retrieved by keyword query,and system finds other documents similar to these Vector space model:define an n-dimensional space,where n is the number of words in the document set. Vector for document d goes from origin to a point whose ith coordinate is TF(d,t)/n(t) The cosine of the angle between the vectors of two documents is used as a measure of their similarity Database System Concepts-5th Edition,Sep 2,2005 19.8 @Silberschatz,Korth and Sudarshan
Database System Concepts - 5 19.8 ©Silberschatz, Korth and Sudarshan th Edition, Sep 2, 2005 Similarity Based Retrieval Similarity based retrieval - retrieve documents similar to a given document Similarity may be defined on the basis of common words E.g. find k terms in A with highest TF (d, t ) / n (t ) and use these terms to find relevance of other documents. Relevance feedback: Similarity can be used to refine answer set to keyword query User selects a few relevant documents from those retrieved by keyword query, and system finds other documents similar to these Vector space model: define an n-dimensional space, where n is the number of words in the document set. Vector for document d goes from origin to a point whose i th coordinate is TF (d,t ) / n (t ) The cosine of the angle between the vectors of two documents is used as a measure of their similarity
Relevance Using Hyperlinks Number of documents relevant to a query can be enormous if only term frequencies are taken into account Using term frequencies makes "spamming"easy E.g.a travel agency can add many occurrences of the words "travel"to its page to make its rank very high Most of the time people are looking for pages from popular sites ldea:use popularity of Web site (e.g.how many people visit it)to rank site pages that match given keywords Problem:hard to find actual popularity of site Solution:next slide Database System Concepts-5th Edition,Sep 2,2005 19.9 @Silberschatz,Korth and Sudarshan
Database System Concepts - 5 19.9 ©Silberschatz, Korth and Sudarshan th Edition, Sep 2, 2005 Relevance Using Hyperlinks Number of documents relevant to a query can be enormous if only term frequencies are taken into account Using term frequencies makes “spamming” easy E.g. a travel agency can add many occurrences of the words “travel” to its page to make its rank very high Most of the time people are looking for pages from popular sites Idea: use popularity of Web site (e.g. how many people visit it) to rank site pages that match given keywords Problem: hard to find actual popularity of site Solution: next slide