System and methods for retrieving and generating recommendations of multi-modal documents

A user equipment (108) for providing a breaking news headline across a plurality of domains, said UE (108) comprising;

• a processor and a receiver, wherein the processor (222) operatively coupled to a plurality of first computing devices (104), the processor (222) coupled with a memory (224), wherein said memory (224) stores instructions which when executed by the one or more processors (222) causes said system (110) to:
• receive, by the receiver, one or more first content items from the plurality of first computing devices (104), the one or more first content items pertaining to a plurality of news headlines received in a plurality of languages, wherein the one or more first content items are in any or a combination of an audio, an image, a video and a textual form;
• receive, by the receiver, one or more second content items from the plurality of first computing devices (104), the one or more second content items pertaining to a plurality of stories received in a plurality of languages and associated with the one or more news headlines, wherein the one or more second content items are in any or a combination of an audio, an image, a video and a textual form;
• extract, by the processor, a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines;
• extract, by the processor, a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories;
• based on the extracted first set of attributes, determine, by using a machine learning (ML) engine (214), a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the processors (222);
• assign, by the processor, the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines;
• and
• generate, by the processor, a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the
• similarity score associated with the one or more first content items.

A method for providing a breaking news headline across a plurality of domains, said method comprising;

• receiving, by one or more processors (202), one or more first content items from the plurality of first computing devices (104), the one or more first content items pertaining to a plurality of news headlines received in a plurality of languages, wherein the one or more first content items are in any or a combination of an audio, an image, a video and a textual form, wherein the one or more processors (202) are operatively coupled to the plurality of first computing devices (104), the one or more processors (202) coupled with a memory (204) that stores instructions executed by the one or more processors (202);
• receiving, by the one or more processors (202), one or more second content items from the plurality of first computing devices (104), the one or more second content items pertaining to a plurality of stories received in a plurality of languages and associated with the one or more news headlines, wherein the one or more second content items are in any or a combination of an audio, an image, a video and a textual form;
• extracting, by the one or more processors (202), a first set of attributes from the one or more first content items, the first set of attributes pertaining to one or more breaking news headlines;
• extracting, by the one or more processors (202), a second set of attributes from the one or more second content items, the second set of attributes pertaining to any or a combination of one or more breaking news stories;
• based on the extracted first set of attributes, determining, by using a machine learning (ML) engine (214), a similarity score between the one or more first content items and the one or more breaking news headlines, wherein the ML engine is associated with the one or more processors (202);
• assigning, by the ML engine (214), the similarity score to each of the one or more first content items according to the similarity present with the one or more first content items and the one or more breaking news headlines;
• and
• generating, by the ML engine (214), a recommendation list in any ascending or descending order of the similarity score, wherein the recommendation list comprises an ordered list of the one or more first content items based on the ascending or descending order of the similarity score associated with the one or more first content items.