Methods, systems and techniques for blending online content from multiple disparate content sources including a personal content source or a semi-personal content source

A system having at least one processor, storage, and a communication platform for providing content, the system comprising:

• an input request processing unit implemented on the at least one processor and configured to receive a request for content from a person;
• a person-centric index system implemented on the at least one processor and configured to construct a person centric space including a plurality of data sources, wherein information in each of the data sources is obtained by digesting and cross-linking information from a private space, a semi-private space, and a public space;
• an intent engine implemented on the at least one processor and configured to estimate an intent of the person based on previous interactions of the person with the person centric space;
• a first content retriever implemented on the at least one processor and configured to obtain first content from a first data source of the plurality of data sources based on the request, and
• identify one or more cross-linking keys from the first content;
• a second content retriever implemented on the at least one processor and configured to obtain second content from a second data source of the plurality of data sources based on the identified cross-linking keys, the second data source being different than the first data source;
• a content attribute extractor implemented on the at least one processor and configured to extract a first attribute set from the first content via a first model, and a second attribute set from the second content via a second model, wherein the first model and the second model are selected based on the first data source and second data source, respectively;
• a feature space selector implemented on the at least one processor and configured to determine, based on the first content and the second content, a feature space including a plurality of features;
• a feature generator implemented on the at least one processor and configured to
• generate a first normalized feature set by mapping the first attribute set the feature space, and
• generate a second normalized feature set by mapping the second attribute set to the feature space;
• a content scoring engine implemented on the at least one processor and configured to determine, based on the first normalized feature set and the second normalized feature set, a rank score for each of the first content and the second content, wherein the rank score represents relevance of a corresponding content to the request;
• and
• a blending engine implemented on the at least one processor and configured to:
• blend the first content and the second content to generate a blended content stream based on the estimated intent, wherein the first content and the second content are ranked in the blended content stream based on the rank scores, and
• generate an answer card to be provided to the person in response to the request,
• wherein the answer card includes the blended content stream.

A non-transitory machine-readable medium having information recorded thereon for providing query suggestions, wherein the information, when read by the machine, causes the machine to perform operations comprising:

• receiving a request for content from a person;
• constructing, via a person-centric system, a person centric space including a plurality of data sources, wherein information in each of the data sources is obtained by digesting and cross-linking information from a private space, a semi-private space, and a public space;
• estimating an intent of the person based on previous interactions of the person with the person centric space;
• obtaining first content from a first data source of the plurality of data sources based on the request;
• identifying one or more cross-linking keys from the first content;
• obtaining second content from a second data source of the plurality of data sources based on the identified cross-linking keys, the second data source being different than the first data source;
• extracting a first attribute set from the first content via a first model, and a second attribute set from the second content via a second model, wherein the first model and the second model are selected based on the first data source and second data source, respectively;
• determining, based on the first content and the second content, a feature space including a plurality of features;
• generating a first normalized feature set by mapping the first attribute set to the feature space;
• generating a second normalized feature set by mapping the second attribute set to the feature space;
• determining, based on the first normalized feature set and the second normalized feature set, a rank score for each of the first content and the second content, wherein the rank score represents relevance of a corresponding content to the request;
• blending the first content and the second content to generate a blended content stream based on the estimated intent, wherein the first content and the second content are ranked in the blended content stream based on the rank scores;
• and
• generating an answer card to be provided to the person in response to the request, wherein the answer card includes the blended content stream.