28. The method of claim 26 comprising partitioning the inverted lists into several groups based on their corresponding query keywords, where each query keyword w has a group of inverted lists, producing a list of record IDs sorted on their scores with respect to this keyword, and using a top-k algorithm to find the k best records for the query.
28. The method of claim 26 comprising partitioning the inverted lists into several groups based on their corresponding query keywords, where each query keyword w has a group of inverted lists, producing a list of record IDs sorted on their scores with respect to this keyword, and using a top-k algorithm to find the k best records for the query. 29. The method of claim 28 where using a top-k algorithm to find the k best records for the query comprises for each group of inverted lists for the query keyword w, retrieving the next most relevant record ID for w by building a max heap on the inverted lists comprising maintaining a cursor on each inverted list in the group, where the heap is comprised of the record IDs pointed by the cursors so far, sorted on the scores of the similar keywords in these records since each inverted list is already sorted based on the weights of its keyword in the records and all the records on this list share the same similarity between this keyword and the query keyword w the list is also sorted based on the scores of this keyword in these records, retrieving the next best record from this group by popping the top element from the heap, incrementing the cursor of the list of the popped element by one, and pushing the new element of this list to the heap, ignoring other lists that may produce this popped record, since their corresponding scores will no longer affect the score of this record with respect to the query keyword w.