Abstract: There is disclosed a computer-implemented method and system for generating a set of training objects for training a machine learning algorithm (MLA) to determine query similarity based on textual content thereof, the MLA executable by the system. The method comprises retrieving, from a search log database of the system, a first query and other queries with associated search results. The method then comprises selecting a subset of query pairs such that: a query difference in queries in the pair is minimized and a results difference in respective search results is maximized.

Abstract: A method and a server for generating a meta-feature for ranking documents by a machine learning algorithm (MLA). A past query having been previously submitted on a server is acquired, and a set of past documents having been presented as search results in response to the past query is acquired, where each respective document includes a plurality of features, and respective values for the plurality of features. The meta-feature is generated, where a respective value of the meta-feature for a respective document is based on: a respective value of a given feature of the plurality of features for the respective document, and a value of a parameter associated with the set of past documents. The meta-feature is validated based on its usefulness for ranking future search engine results pages (SERPs). The MLA is then trained to generate the meta-feature for ranking documents in response to a new query.

Abstract: A method and a system for ranking a document in response to a query, the document having no value for a given feature with respect to the query. A set of documents relevant to the query is generated. The document is selected, and a set of past queries having presented the document as a search result are retrieved. Respective values for the given feature for the document with respect to the set of past queries are retrieved. A respective similarity parameter is determined between the query and each of the set of past queries. The value of the given feature for the document is generated based at least in part on the respective similarity parameter and the respective value for the given feature of at least one past query. The set of documents including the document is ranked based in part on the given 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 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: There is disclosed a computer-implemented method and system for generating a set of training objects for training a machine learning algorithm (MLA) to determine query similarity based on textual content thereof, the MLA executable by the system. The method comprises retrieving, from a search log database of the system, a first query and other queries with associated search results.

Abstract: A method and a server for generating a meta-feature for ranking documents by a machine learning algorithm (MLA). A past query having been previously submitted on a server is acquired, and a set of past documents having been presented as search results in response to the past query is acquired, where each respective document includes a plurality of features, and respective values for the plurality of features. The meta-feature is generated, where a respective value of the meta-feature for a respective document is based on: a respective value of a given feature of the plurality of features for the respective document, and a value of a parameter associated with the set of past documents. The meta-feature is validated based on its usefulness for ranking future search engine results pages (SERPs). The MLA is then trained to generate the meta-feature for ranking documents in response to a new query.

Abstract: A method and a system for ranking a document in response to a query, the document having no value for a given feature with respect to the query. A set of documents relevant to the query is generated. The document is selected, and a set of past queries having presented the document as a search result are retrieved. Respective values for the given feature for the document with respect to the set of past queries are retrieved. A respective similarity parameter is determined between the query and each of the set of past queries. The value of the given feature for the document is generated based at least in part on the respective similarity parameter and the respective value for the given feature of at least one past query. The set of documents including the document is ranked based in part on the given 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 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 a system for generating a plurality of annotation vectors for a document, the plurality of annotation vectors to be used as features by a first machine-learning algorithm (MLA) for information retrieval, the method executable by a second MLA on a server, the method comprising: retrieving the document, the document having been indexed by a search engine server, retrieving a plurality of queries having been used to discover the document, retrieving a plurality of user interaction parameters for each one of the plurality of queries, generating the plurality of annotation vectors, each annotation vector being associated with a respective query of the plurality of queries, each annotation vector of the plurality of annotation vectors including an indication of: the respective query, a plurality of query features, the plurality of query features being at least indicative of linguistic features, and the plurality of user interaction parameters.