Method and apparatus for selecting a network resource as a source of content for a recommendation system

• US 10,706,325 B2
• Filed: 05/26/2017
• Issued: 07/07/2020
• Est. Priority Date: 07/07/2016
• Status: Active Grant

• Current Assignee: Direct Cursus Technology LLC
• Original Assignee: Yandex Europe AG (Yandex N.V.)
• Inventors: Lamburt, Victor Grigorievich, Lifar, Igor Igorevich
• Primary Examiner(s): Sefcheck, Gregory B
• Assistant Examiner(s): Asefa, Debebe A
• Application Number:
  US15/606,326
• Publication Number:
  US 20180014038A1
• Time in Patent Office: 1,138 Days
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: H04L 47/80 Actions related to the user profile or the type of traffic
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08

Abstract

There are disclosed a method of and a system for selecting a network resource as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a recommendation for a given user of the recommendation system. The method comprises, for a network resource, receiving, by the server, a plurality of features associated with a network resource to be processed; generating given network resource profile for the network resource, the given network resource profile being based on the plurality of features; executing a machine learning algorithm in order to determine a source suitability parameter for the network resource, selecting at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold.

Claims

1. A method of selecting a network resource as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a content recommendation for a given user of the recommendation system, the recommendation system being executed at a server coupled to a communication network, the method comprising:

prior to acquiring a content recommendation request from the given user;

acquiring, by the server, an indication of the network resource;

receiving, by the server, a plurality of features associated with the network resource;

generating, by the server, a given network resource profile for the network resource, the given network resource profile being based on the plurality of features;

executing, by the server, a machine learning algorithm in order to, based on the given network resource profile, determine a source suitability parameter for the network resource, the source suitability parameter being indicative of the network resource being susceptible to publishing the content item that can be processed by the recommendation system,

acquiring the content recommendation request from the given user;

selecting at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold, the at least one content item for submitting to the recommendation system for processing; and

generating the content recommendation for the given user, the content recommendation including the least one content item from the network resource.

2. The method of claim 1, wherein:

the machine learning algorithm having been trained, prior to said acquiring, to determine the source suitability parameter based on training web site profiles generated with an associated plurality of training features at least some of which are of a same category as the plurality of features.

3. The method of claim 1, wherein the plurality of features includes at least an absolute feature and a relative feature.

4. The method of claim 3, wherein the absolute feature and the relative feature are base features and wherein the method further comprises:

generating at least one derivative feature based on at least one of the absolute feature and the relative feature; and

wherein

the given web site profile is further based on the at least one derivative feature.

5. The method of claim 1, wherein the plurality of features includes an indication of a proportion of visit sources to the network resource.

6. The method of claim 1, wherein the plurality of features includes at least one relative feature selected from:

hits per visit (HPV);

sessions per visit (SPV);

dated-pages per visit (DPV);

home page per visit (MPV);

time spent on the network resource (MTIME);

last domain per session (LPS);

first domain per session (FPS);

hits per session (HPS);

visits per user (VPU);

hits per user (HPU);

sessions per user (SPU);

dated-pages per hit (DPH);

home pages per hit (MPH); and

an indication of a visit source being one of;

direct access visit-source (TT_GR);

search engine visit-source (TT_SE);

social network visit-source (TT_SN);

electronic message visit-source (TT_IM);

URL-condenser visit-source (TT_US); and

ad-platform visit-source (TT_AD).

7. The method of claim 1, wherein the plurality of features includes at least one of:

a number of users visiting the network resource per time interval, number of visits per time interval, number of transitions within various web pages within the network resource per time interval, and a number of unique sessions with the network resource per time interval.

8. The method of claim 1, wherein the plurality of features is a subset of all possible features, the method further comprising executing a feature selection to define the subset.

9. The method of claim 8, wherein the subset of all possible features consists of:

at least one of;

a proportion of a landing page of the network resource visits within an initial transition to the network resource within a session and a proportion of the landing page visits during transitions within the network resource;

at least one of;

a proportion of a web page associated with an URL containing a date being a first page visited within the initial transition to the network resource and a proportion of the web pages within the network resource visited during the session, the web pages being associated with an URL containing a date;

a proportionate profile of visit sources for the network resource, the visit sources being selected from;

search engine/social networks/mail and messenger application/URL condensers/direct visits;

a proportion of session where the network resource was last in the session.

10. The method of claim 1, wherein the source suitability parameter is one of a suitable and non-suitable.

11. The method of claim 10, wherein the source suitability parameter is determined using at least one of:

a logistical regression analysis;

naive Bayes algorithm;

k-nearest neighbor algorithm;

random forest algorithm; and

hard voting algorithm.

12. A server comprising:

a data storage medium;

a network interface configured for communication over a communication network;

a processor operationally coupled to the data storage medium and the network interface, the processor being configured to;

prior to acquiring a content recommendation request from the given user;

acquire an indication of the network resource, the network resource to be processed to determine its suitability as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a recommendation for a given user of the recommendation system;

receive a plurality of features associated with the network resource;

generate a given network resource profile for the network resource, the given network resource profile being based on the plurality of features;

execute a machine learning algorithm in order to, based on the given network resource profile, determine a source suitability parameter for the network resource, the source suitability parameter being indicative of the network resource being susceptible to publishing a content item that can be processed by the recommendation system;

acquire the content recommendation request from the given user;

select at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold, the at least one content item for submitting to the recommendation system for processing; and

generate the content recommendation for the given user, the content recommendation including the at least one content item from the network resource.

13. The server of claim 12, wherein:

the machine learning algorithm has been trained, prior to said acquiring, to determine the source suitability parameter based on training web site profiles generated with an associated plurality of training features at least some of which are of a same category as the plurality of features.

14. The server of claim 12, wherein the plurality of features includes at least an absolute feature and a relative feature.

15. The server of claim 14, wherein the absolute feature and the relative feature are base features and wherein the processor is further configured to:

generate at least one derivative feature based on at least one of the absolute feature and the relative feature; and

wherein

the given web site profile is further based on the at least one derivative feature.

16. The server of claim 12, wherein the plurality of features includes an indication of a proportion of visit sources to the network resource.

17. The server of claim 12, wherein the plurality of features includes at least one relative feature selected from:

hits per visit (HPV);

sessions per visit (SPV);

dated-pages per visit (DPV);

home page per visit (MPV);

time spent on the network resource (MTIME);

last domain per session (LPS);

first domain per session (FPS);

hits per session (HPS);

visits per user (VPU);

hits per user (HPU);

sessions per user (SPU);

dated-pages per hit (DPH);

home pages per hit (MPH); and

an indication of a visit source being one of;

direct access visit-source (TT_GR);

search engine visit-source (TT_SE);

social network visit-source (TT_SN);

electronic message visit-source (TT_IM);

URL-condenser visit-source (TT_US); and

ad-platform visit-source (TT_AD).

18. The server of claim 12, wherein the plurality of features includes at least one of:

a number of users visiting the network resource per time interval, number of visits per time interval, number of transitions within various web pages within the network resource per time interval, and a number of unique sessions with the network resource per time interval.

19. The server of claim 12, wherein the plurality of features is a subset of all possible features, the method further comprising executing a feature selection to define the subset;

and wherein the subset of all possible features consists of;

at least one of;

a proportion of a landing page of the network resource visits within an initial transition to the network resource within a session and a proportion of the landing page visits during transitions within the network resource;

at least one of;

a proportion of a web page associated with an URL containing a date being a first page visited within the initial transition to the network resource and a proportion of the web pages within the network resource visited during the session, the web pages being associated with an URL containing a date;

a proportionate profile of visit sources for the network resource, the visit sources being selected from;

search engine/social networks/mail and messenger application/URL condensers/direct visits;

a proportion of session where the network resource was last in the session.