Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser. Personalized results are generated based on collected data for a user if possible. If not possible, fallback results are provided by a front end server based on values provided in a browser request matching entries in a fallback database accessed by the front end server. Default results may be provided where the fallback database does not provide a result.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser. Profile values are generated by clustering users using first parameters and scoring the clusters using second parameters, the first and second parameters being data describing user behavior. Profile values may be generated by processing cluster scores according to a mapping function.

Abstract: Data from browser requests is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser. Personalized results are generated based on collected data for a user if possible. If not possible, fallback results are provided by a front end server based on values provided in a browser request matching entries in a fallback database accessed by the front end server. Default results may be provided where the fallback database does not provide a result.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.

Abstract: Browser requests are received and data included in it is added to a vector. If explicit identification information (username, cookie data, etc.) is present, the vector is associated with a pre-existing user record, which is then updated. If not, candidate user records may be identified according to correspondence with values in the vector. This may include comparing hashes of one or more values to identify similarities. Candidate vectors may be eliminated by identifying inconsistency in OS, device, and browser information. Probability assigned to each candidate vector may be adjusted, e.g., reduced, in response to inconsistency in other data relating to a browser, device, or of a more global nature, e.g. time zone, user attributes, etc. Records associated with different devices may be associated with one another by evaluating hashes of data values submitted by a user on the different devices. Where the hash values of two records intersect, they may be merged with one another.