Abstract: A method and server for storing documents in a database system is disclosed. The method includes acquiring document data associated with the documents. For each document, a Machine Learning Algorithm is employed to generate respective document vectors based on the respective document data. The method also includes storing the documents as groups of documents in the database system. Each group of documents is associated with a respective group vector. A given group of documents has documents associated with document vectors that are in a spatial proximity to the respective group vector.

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 server for ranking documents in response to a current query are disclosed. The documents are to be presented on a SERP. A database stores stored search pairs in association with respective pair-specific values. The method comprises for the current query, ranking, by a MLA, relevant documents to be included in the SERP which have preliminary ranks. The current query and a respective relevant document form a current search pair. The method comprises, for a given current search pair, generating a rank-adjustment score associated with a stored search pair based on: the pair-specific value of the stored search pair, and a pair-wise similarity between the current search pair and the stored search pair. The method comprises, for the current query, re-ranking a relevant document of the current search pair on the SERP using the associated rank-adjustment score.

Abstract: A method and server for storing documents in a database system is disclosed. The method includes acquiring document data associated with the documents. For each document, a Machine Learning Algorithm is employed to generate respective document vectors based on the respective document data. The method also includes storing the documents as groups of documents in the database system. Each group of documents is associated with a respective group vector. A given group of documents has documents associated with document vectors that are in a spatial proximity to the respective group vector.

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 server for ranking documents in response to a current query are disclosed. The documents are to be presented on a SERP. A database stores stored search pairs in association with respective pair-specific values. The method comprises for the current query, ranking, by a MLA, relevant documents to be included in the SERP which have preliminary ranks. The current query and a respective relevant document form a current search pair. The method comprises, for a given current search pair, generating a rank-adjustment score associated with a stored search pair based on: the pair-specific value of the stored search pair, and a pair-wise similarity between the current search pair and the stored search pair. The method comprises, for the current query, re-ranking a relevant document of the current search pair on the SERP using the associated rank-adjustment score.