SYSTEMS AND METHODS FOR PROVIDING CONTENT RECOMMENDATIONS
Systems and associated methods are described for providing content recommendations. The system receives a plurality of sets of range values, each of which corresponds to a respective one of a plurality of parameters for recommending the content. The system selects different values within each of the plurality of sets of range values over time and provides a plurality of content recommendations to users based on the selected different values. The system then analyze users' behavior in response to the provided plurality of content recommendations. The system further updates at least one set of range values based on the analyzed users' behavior.
1 - 20 . (canceled)
21 . A method comprising:
receiving, via control circuitry, a plurality of parameter weight ranges, each parameter weight range including a respective minimum value and a respective maximum value, each parameter weight range corresponding to a respective parameter of a plurality of parameters for generating recommendations of media content;
transmitting the plurality of parameter weight ranges to a machine learning model;
receiving a first media content recommendation generated, using the plurality of parameter weight ranges, by the machine learning model;
generating, for display by a user media device, the first media content recommendation;
obtaining a user engagement level by analyzing user behavior in response to the first media content recommendation;
adjusting via the control circuitry, using the user engagement level, at least one parameter weight range of the plurality of parameter weight ranges to obtain an adjusted plurality of parameter weight ranges;
transmitting the adjusted plurality of parameter weight ranges to the machine learning model;
receiving a second media content recommendation generated, using the adjusted plurality of parameter weight ranges, by the machine learning model; and
generating, for display by the user media device, the second media content recommendation.
22 . The method of claim 21 , wherein the analyzing the user behavior comprises:
tracking user interaction with the first media content recommendation; and
analyzing the user interaction to determine the user engagement level with the first media content recommendation.
23 . The method of claim 22 , wherein the adjusting the at least one parameter weight range comprises shifting the at least one parameter weight range based on the user engagement level with the first media content recommendation.
24 . The method of claim 23 , wherein the shifting further comprises:
moving the respective minimum value higher when the user engagement level with a plurality of media content recommendations, including the first media content recommendation, is determined to be higher for higher selected values.
25 . The method of claim 23 , wherein the shifting further comprises:
moving the respective minimum value lower and moving the respective maximum value lower when the user engagement level with a plurality of media content recommendations including the first media content recommendation is determined to be lower.
26 . The method of claim 21 , wherein the plurality of parameter weight ranges is received from a media content provider.
27 . The method of claim 21 , wherein parameters of the plurality of parameter weight ranges comprise one or more of popularity of media content among users, recency of media content, media content indicated as favorite, media content liked by users, media content recommended by media content critics.
28 . The method of claim 21 , wherein the analyzing and the adjusting are performed according to a media content recommendation updating schedule.
29 . A system comprising:
a memory; and
control circuitry configured:
to receive a plurality of parameter weight ranges, each parameter weight range including a respective minimum value and a respective maximum value, each parameter weight range corresponding to a respective parameter of a plurality of parameters for generating recommendations of media content;
to store in the memory the plurality of parameter weight ranges;
to transmit the plurality of parameter weight ranges to a machine learning model;
to receive a first media content recommendation generated, using the plurality of parameter weight ranges, by the machine learning model;
to generate, for display by a user media device, the first media content recommendation;
to obtain a user engagement level by analyzing user behavior in response to the first media content recommendation;
to adjust, using the user engagement level, at least one parameter weight range of the plurality of parameter weight ranges to obtain an adjusted plurality of parameter weight ranges;
to transmit the adjusted plurality of parameter weight ranges to the machine learning model;
to receive a second media content recommendation generated, using the adjusted plurality of parameter weight ranges, by the machine learning model; and
to generate, for display by the user media device, the second media content recommendation.
30 . The system of claim 29 , wherein the analyzing the user behavior comprises:
tracking user interaction with the first media content recommendation; and
analyzing the user interaction to determine the user engagement level with the first media content recommendation.
31 . The system of claim 30 , wherein the adjusting the at least one parameter weight range comprises shifting the at least one parameter weight range based on the user engagement level with the first media content recommendation.
32 . The system of claim 31 , wherein the shifting further comprises:
moving the respective minimum value higher when the user engagement level with a plurality of media content recommendations, including the first media content recommendation, is determined to be higher for higher selected values.
33 . The system of claim 31 , wherein the shifting further comprises:
moving the respective minimum value lower and moving the respective maximum value lower when the user engagement level with a plurality of media content recommendations including the first media content recommendation is determined to be lower.
34 . The system of claim 29 , wherein the plurality of parameter weight ranges is received from a media content provider.
35 . The system of claim 29 , wherein parameters of the plurality of parameter weight ranges comprise one or more of popularity of media content among users, recency of media content, media content indicated as favorite, media content liked by users, media content recommended by media content critics.
36 . The system of claim 29 , wherein the analyzing and the adjusting are performed according to a media content recommendation updating schedule.