US 2022/0188698 A1 · MACHINE LEARNING TECHNIQUES FOR WEB RESOURCE INTEREST DETECTION

Abstract

Disclosed embodiments include an event processor that identifies events generated by an entity from various resources. The event processor generates a resource cluster interest score based on the events indicating an interest level of the entity in multiple hostname resources belonging to a first party. The event processor identifies a topic cluster including multiple topics and generates a topic cluster interest score indicating an interest level of the entity in the topics. The event processor generates a weighted intent score based on the resource interest score and the topic cluster interest score. The weighted intent score provides an indication of when the entity is interested in consuming resources, or interested in products/services, provided by the first party. Other embodiments may be described and/or claimed.

Claims20

Dependent claims

One or more non-transitory computer readable media (NTCRM) comprising instructions, wherein execution of the instructions by one or more processors is operable to cause a computing device to:

obtain a first set of network events generated by client devices, each network event of the first set of network events including a first network address of an information object and a second network address of a device that accessed the information object;
generate a second set of network events by replacement of the first network address with a hostname resource and replacement of the second network address with a predicted entity;
generate one or more machine learning (ML) features from the second set of network addresses;
and
generate a resource interest score based on the one or more ML features, the resource interest score indicating an interest level of the entity in the hostname resource.

2 more elements

An apparatus to be employed as a resource interest detector, the apparatus comprising:

at least one processor;
and
a memory device communicatively coupled with the at least one processor, the memory device storing one or more sequences of instructions, and the at least one processor is configurable to:
operate a consumption event transform to convert a set of raw network events into a set of hostname events, each hostname event of the set of hostname events indicating a hostname resource and a predicted entity from which the hostname resource was accessed;
operate a resource interest feature (RIF) generator to generate a set of RIFs from the set of hostname events for a time period, the set of RIFs indicating an interest level of the entity in the hostname resources during the time period;
operate an interest score generator (ISG) to generate a resource interest score vector for the time period based on a combination of the set of RIFs, the resource interest score vector including a resource interest score for each hostname resource indicated by the set of hostname events;
operate a resource cluster ISG (RCISG) to calculate a resource cluster interest score based on the resource interest scores of the resource interest score vector;
operate a topic cluster interest score generator (TCISG) to calculate topic cluster interest score based on a set of topic interest scores of a topic interest score vector, the set of topic interest scores being topic interest scores generated for each hostname resource;
and
operate a weighted intent score generator (WISG) to generate weighted intent score based on a combination of resource cluster interest score and the topic cluster interest score.

7 more elements

Dependencies are inferred from the claim text.

Machine learning techniques for web resource interest detection

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Machine learning techniques for web resource interest detection

Life of this patent

Abstract

Claims20