User behavior model for contextual personalized recommendation

One or more computer storage media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising:

• controlling the one or more processors configured with executable instructions to perform:
• receiving context information about a user of a mobile device including a local time of the user and a location of the user;
• ranking entity types according to relevance to the user, the relevance to the user of the ranked entity types based in part on mass user behavior defined within a query database and based in part on the context information;
• ranking entities within each entity type according to relevance to the user, the relevance to the user of ranked entities within each entity type based in part on mass user behavior defined within the query database and based in part on the context information;
• displaying, within a user interface, a listing of entity types according to the ranking of the entity types, a first entity type in the listing being distinct from a second entity type in the listing;
• representing the first entity type in the listing with a first most relevant entity within the first entity type according to a ranking of entities within the first entity type at the user interface;
• and
• representing the second entity type in the listing with a second most relevant entity within the second entity type according to a ranking of entities within the second entity type at the user interface.

A method to model user behavior for contextual personalized recommendation, comprising:

• controlling the one or more processors configured with executable instructions to perform:
• receiving input, the input being based in part on a selection made from a list of options by a user of a mobile device and based in part on a current context, the list of options being displayed on a screen of the mobile device and being based in part on options available at a local time of the user and a location of the user;
• searching a query database to obtain a query set based on the input, the query database including queries intended to fit circumstances of a plurality of different users of mobile devices;
• weighting queries in the query set, the weighting based in part on comparing the user of the mobile device to other users of other mobile devices, the comparing being based in part on:
• identifying at least one user having conducted a query in the query set;
• determining a frequency at which the user identified as having conducted the query in the query set has conducted the query in the query set;
• determining a similarity between the user from whom the input was received and the user identified as having conducted the query in the query set;
• and
• weighting each query in the query set based on the frequency and the similarity;
• selecting a query from among the weighted queries in the query set;
• searching a network based on the selected query;
• and
• returning results based on the search of the network.