Abstract: A method and a system for ranking search results using a machine learning algorithm (MLA), the MLA having been trained to determine query similarity based on textual content thereof, comprising: receiving a new query, retrieving a plurality of search queries, computing a respective similarity parameter between the new query and each query of the plurality of search queries, selecting a given past query associated with a highest respective similarity parameter, retrieving a set of search results associated with the given past query, each of the set of search results being associated with an annotation including respective search queries, and computing for each respective one of the plurality of respective search queries having been used to access the respective search result, a respective second similarity parameter, and using the respective second similarity parameter as a ranking feature for ranking the associated search results as search results for the new query.

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.