Abstract: A method and server for ranking digital documents for a query are disclosed. T server hosts a search engine associated with an inverted index. The method includes accessing inverted index for retrieving query-independent data for a first document-term pair and a second document-term pair, where the query-independent data is indicative of (i) a term-specific occurrence of the first term in content of the document and (ii) a term-specific occurrence of the second term in the content of the document. The method includes generating a query-dependent feature using the query-independent data and which is indicative of a group occurrence of the first term with the second term in the content of the document. The method includes generating a ranking feature for the given document based on at least the first term, the second term, and the query-dependent feature, and ranking the given document based on at least the ranking feature.

Abstract: A method and server for ranking digital documents for a query are disclosed. T server hosts a search engine associated with an inverted index. The method includes accessing inverted index for retrieving query-independent data for a first document-term pair and a second document-term pair, where the query-independent data is indicative of (i) a term-specific occurrence of the first term in content of the document and (ii) a term-specific occurrence of the second term in the content of the document. The method includes generating a query-dependent feature using the query-independent data and which is indicative of a group occurrence of the first term with the second term in the content of the document. The method includes generating a ranking feature for the given document based on at least the first term, the second term, and the query-dependent feature, and ranking the given document based on at least the ranking feature.

Abstract: A method and server for ranking digital documents for a query are disclosed. T server hosts a search engine associated with an inverted index. The method includes accessing inverted index for retrieving query-independent data for a first document-term pair and a second document-term pair, where the query-independent data is indicative of (i) a term-specific occurrence of the first term in content of the document and (ii) a term-specific occurrence of the second term in the content of the document. The method includes generating a query-dependent feature using the query-independent data and which is indicative of a group occurrence of the first term with the second term in the content of the document. The method includes generating a ranking feature for the given document based on at least the first term, the second term, and the query-dependent feature, and ranking the given document based on at least the ranking feature.

Abstract: A method and a system for ranking search results in response to a current query comprising: receiving an indication of the current query from an electronic device, generating a set of search result relevant to the current search query, retrieving a plurality of past queries, computing a respective similarity parameter between the current query and a respective one of the plurality of past queries, ranking the set of current documents to obtain a ranked set of documents, the ranking being done by a machine learning algorithm (MLA) taking into account inclusion of search terms of at least one past query of the plurality of past queries in a given one of the set of current documents so that inclusion of search terms promotes rank of the given current document, and transmitting a search engine results page (SERP) including the ranked set of documents to the electronic device.

Abstract: A method and a system for ranking search results in response to a current query comprising: receiving an indication of the current query from an electronic device, generating a set of search result relevant to the current search query, retrieving a plurality of past queries, computing a respective similarity parameter between the current query and a respective one of the plurality of past queries, ranking the set of current documents to obtain a ranked set of documents, the ranking being done by a machine learning algorithm (MLA) taking into account inclusion of search terms of at least one past query of the plurality of past queries in a given one of the set of current documents so that inclusion of search terms promotes rank of the given current document, and transmitting a search engine results page (SERP) including the ranked set of documents to the electronic device.