Abstract: A method of determining a conversion rate of a targeted message associated with a point of interest (POI) includes determining a plurality of users using wireless devices that have been exposed to a targeted message associated with the POI within a first pre-determined time period and receiving a plurality of sets of local area communication features of the local area communication modules of the wireless devices. These features are inputted into a Machine Learning Algorithm (MLA) trained using a set of heuristics applied to training sets of local area communication features of training wireless devices. The MLA outputs, based on the set of local area communication features of the local area communication module of the wireless device, a plurality of indications of the conversion rate of the targeted message. A server for executing the method is also provided.

Abstract: A method of determining a Place Visit Lift (PVL) metric by determining a plurality of users using wireless devices that have been exposed to a targeted message associated with the POI within a first pre-determined time period and receiving a plurality of sets of local area communication features of the local area communication modules of the wireless devices. These features are inputted into a Machine Learning Algorithm (MLA) trained using a set of heuristics applied to training sets of local area communication features of training wireless devices. The MLA outputs, based on the set of local area communication features of the local area communication module of the wireless device, a plurality of indications of the conversion rate of the targeted message. The method comprises determining based on the plurality of indications, the at least one of the visitors_site, the bypassers_site, the visitors_non-site, and the bypassers_non-site parameters.

Abstract: A method of determining a fact of a visit of a user to a point of interest (POI) includes receiving a geo-track generated by a wireless device of the user, generating, based on the geo-track, a dwell profile indicative of the wireless device having been in a pre-defined vicinity of the location of the POI over a pre-determined timeframe, and inputting the dwell profile into a specifically trained Machine Learning Algorithm (MLA). Based on the dwell profile, the MLA returns an indication of whether the user visited the POI. A system and server for executing the method are also provided.

Abstract: A method of determining a Place Visit Lift (PVL) metric by determining a plurality of users using wireless devices that have been exposed to a targeted message associated with the POI within a first pre-determined time period and receiving a plurality of sets of local area communication features of the local area communication modules of the wireless devices. These features are inputted into a Machine Learning Algorithm (MLA) trained using a set of heuristics applied to training sets of local area communication features of training wireless devices. The MLA outputs, based on the set of local area communication features of the local area communication module of the wireless device, a plurality of indications of the conversion rate of the targeted message. The method comprises determining based on the plurality of indications, the at least one of the visitors_site, the bypassers_site, the visitors_non-site, and the bypassers_non-site parameters.

Abstract: A method of determining a conversion rate of a targeted message associated with a point of interest (POI) includes determining a plurality of users using wireless devices that have been exposed to a targeted message associated with the POI within a first pre-determined time period and receiving a plurality of sets of local area communication features of the local area communication modules of the wireless devices. These features are inputted into a Machine Learning Algorithm (MLA) trained using a set of heuristics applied to training sets of local area communication features of training wireless devices. The MLA outputs, based on the set of local area communication features of the local area communication module of the wireless device, a plurality of indications of the conversion rate of the targeted message. A server for executing the method is also provided.

Abstract: A method of determining a fact of a visit of a user to a point of interest (POI) includes receiving a wireless device identifier of the user's wireless device and based thereon determining a set of local area communication features of a local area communication module of the wireless device. These features are inputted into a Machine Learning Algorithm (MLA) trained using a set of heuristics applied to training sets of local area communication features of training wireless devices. The MLA outputs, based on the set of local area communication features of the local area communication module of the wireless device, an indication of whether the user visited the POI. A server for executing the method is also provided.

Abstract: Methods and systems for selecting a selected-sub-set of features from a plurality of features for training a machine learning module, the training of the machine learning module to enable classification of an electronic document to a target label, the plurality of features associated with the electronic document. In one embodiment, the method comprises analyzing a given training document to extract the plurality of features, and for a given not-yet-selected feature of the plurality of features: generating a set of relevance parameters iteratively, generating a set of redundancy parameters iteratively and determining a feature significance score based on the set of relevance parameters and the set of redundancy parameters. The method further comprises selecting a feature associated with a highest value of the feature significance score and adding the selected feature to the selected-sub-set of features.

Abstract: A system and method of ranking a plurality of documents on a search engine results page (SERP) in response to a query, the method comprising: computing a ranking score for each document, a given ranking score being indicative of an original rank of a respective document; computing a class association value for each document, a given class association value being a probability of a respective document to be associated with a predetermined class of documents having been determined by an operator, a given class association value being instrumental in increasing a magnitude of change of the original rank of the respective document; computing an amended ranking score for each document based on a respective ranking score and a respective class association value; and triggering a display of the SERP with the documents being ranked based on the respective amended ranking score.

Abstract: A method of determining a fact of a visit of a user to a point of interest (POI) includes receiving a wireless device identifier of the user's wireless device and based thereon determining a set of local area communication features of a local area communication module of the wireless device. These features are inputted into a Machine Learning Algorithm (MLA) trained using a set of heuristics applied to training sets of local area communication features of training wireless devices. The MLA outputs, based on the set of local area communication features of the local area communication module of the wireless device, an indication of whether the user visited the POI. A server for executing the method is also provided.

Abstract: A method of determining a fact of a visit of a user to a point of interest (POI) includes receiving a geo-track generated by a wireless device of the user, generating, based on the geo-track, a dwell profile indicative of the wireless device having been in a pre-defined vicinity of the location of the POI over a pre-determined timeframe, and inputting the dwell profile into a specifically trained Machine Learning Algorithm (MLA). Based on the dwell profile, the MLA returns an indication of whether the user visited the POI. A system and server for executing the method are also provided.

Abstract: A system and method of ranking a plurality of documents on a search engine results page (SERP) in response to a query, the method comprising: computing a ranking score for each document, a given ranking score being indicative of an original rank of a respective document; computing a class association value for each document, a given class association value being a probability of a respective document to be associated with a predetermined class of documents having been determined by an operator, a given class association value being instrumental in increasing a magnitude of change of the original rank of the respective document; computing an amended ranking score for each document based on a respective ranking score and a respective class association value; and triggering a display of the SERP with the documents being ranked based on the respective amended ranking score.

Abstract: Methods and systems for selecting a selected-sub-set of features from a plurality of features for training a machine learning module, the training of the machine learning module to enable classification of an electronic document to a target label, the plurality of features associated with the electronic document. In one embodiment, the method comprises analyzing a given training document to extract the plurality of features, and for a given not-yet-selected feature of the plurality of features: generating a set of relevance parameters iteratively, generating a set of redundancy parameters iteratively and determining a feature significance score based on the set of relevance parameters and the set of redundancy parameters. The method further comprises selecting a feature associated with a highest value of the feature significance score and adding the selected feature to the selected-sub-set of features.