Abstract: The present teaching relates to exploring a personal interest space based on a decision tree. In one example, information related to a personal interest space associated with a user is obtained. The personal interest space includes at least one channel of content that the user is interested in. A decision tree created based on information related to a plurality of users with respect to a plurality of channels of content is obtained. Each node of the decision tree is associated with at least one of the plurality of channels of content. A first node of the decision tree is identified based on the information related to the personal interest space associated with the user. A question for the user in connection with the first node of the decision tree is determined. An answer to the question is received from the user. A second node of the decision tree is identified based on the answer.

Abstract: The present teaching relates to exploring a personal interest space based on a decision tree. In one example, information related to a personal interest space associated with a user is obtained. The personal interest space includes at least one channel of content that the user is interested in. A decision tree created based on information related to a plurality of users with respect to a plurality of channels of content is obtained. Each node of the decision tree is associated with at least one of the plurality of channels of content. A first node of the decision tree is identified based on the information related to the personal interest space associated with the user. A question for the user in connection with the first node of the decision tree is determined. An answer to the question is received from the user. A second node of the decision tree is identified based on the answer.

Abstract: The present teaching relates to providing content to a user. Information related to a user is obtained. One or more channels are determined for the user from a plurality of channels based on the information, wherein each of the plurality of channels includes one or more online content items. A personal interest space is established associated with the user based on the information related to the user and the one or more channels. Content is provided in the one or more channels to the user based on information related to the personal interest space.

Abstract: The present teaching relates to providing content to a user. Information related to a user is obtained. One or more channels are determined for the user from a plurality of channels based on the information, wherein each of the plurality of channels includes one or more online content items. A personal interest space is established associated with the user based on the information related to the user and the one or more channels. Content is provided in the one or more channels to the user based on information related to the personal interest space.

Abstract: The present teaching relates to providing content to a user. Information related to a user is obtained. One or more channels are determined for the user from a plurality of channels based on the information, wherein each of the plurality of channels includes one or more online content items. A personal interest space is established associated with the user based on the information related to the user and the one or more channels. Content is provided in the one or more channels to the user based on information related to the personal interest space.

Abstract: Method, system, and programs for online advertising are disclosed. One or more targets associated with an entity are identified based on a first piece of information related to each target and a second piece of information related to the entity. The one or more targets are considered as likely being interested in content that can be made available by the entity. A connection is established between the entity and each identified target through a bi-directional communication channel. Activities between the entity and each target through the bi-directional communication channel are monitored. The entity delivers the content to an identified target through the bi-directional communication channel established between the entity and the identified target. In response to the delivered content, the identified target is able to provide a feedback to the entity through the bi-directional communication channel.

Abstract: Method, system, and programs for recommending content to a user. First information related to one or more previous users is first obtained. A model that maps from users to topics of interest is then established based on the first information related to the one or more previous users. Second information related to the current user is also obtained. One or more topics of interest are identified for the current user based on the model. Content is recommended to the current user in accordance with the one or more topics of interest for the current user. Eventually, an updated model is generated by integrating information associated with the current user with the model established based on the first information related to the one or more previous users. The information associated with the current user includes the second information related to the current user.

Abstract: Method, system, and programs for hybrid information query. A request is first received from a user associated with a hybrid query. The hybrid query is expressed in accordance with an input in terms of one of a user, a feature, and a document, and a desired hybrid query result in terms of one of a user, a feature, and a document. A mapping is then determined between the input and the desired hybrid query result. A hybrid model is established based on hybrid information collected and associated with one or more users. The mapping is performed based on the hybrid model to obtain the desired hybrid query result based on the input. Eventually, the desired hybrid query result is provided as a response to the hybrid query.

Abstract: The present teaching relates to a multi-level content platform. In one example, an interface corresponding to an application eco-space is presented to a user. The application eco-space comprises one or more applications. First information associated with the user is obtained via the interface. The first information characterizes interest of the user. At least one of the one or more applications is identified to be recommended to the user based on the first information. The at least one of the one or more applications is deployed within the application eco-space in a manner consistent with the interest of the user based on the first information.

Abstract: The present teaching relates to exploring a personal interest space based on a decision tree. In one example, information related to a personal interest space associated with a user is obtained. The personal interest space includes at least one channel of content that the user is interested in. A decision tree created based on information related to a plurality of users with respect to a plurality of channels of content is obtained. Each node of the decision tree is associated with at least one of the plurality of channels of content. A first node of the decision tree is identified based on the information related to the personal interest space associated with the user. A question for the user in connection with the first node of the decision tree is determined. An answer to the question is received from the user. A second node of the decision tree is identified based on the answer.

Abstract: The present teaching relates to providing content to a user. Information related to a user is obtained. One or more channels are determined for the user from a plurality of channels based on the information, wherein each of the plurality of channels includes one or more online content items. A personal interest space is established associated with the user based on the information related to the user and the one or more channels. Content is provided in the one or more channels to the user based on information related to the personal interest space.

Abstract: Methods and systems are disclosed that relate to ranking functions for multiple different domains. By way of example but not limitation, ranking functions for multiple different domains may be trained based on inter-domain loss, and such ranking functions may be used to rank search results from multiple different domains so that they may be blended without normalizing relevancy scores.

Abstract: Method, system, and programs for hybrid information query. A request is first received from a user associated with a hybrid query. The hybrid query is expressed in accordance with an input in terms of one of a user, a feature, and a document, and a desired hybrid query result in terms of one of a user, a feature, and a document. A mapping is then determined between the input and the desired hybrid query result. A hybrid model is established based on hybrid information collected and associated with one or more users. The mapping is performed based on the hybrid model to obtain the desired hybrid query result based on the input. Eventually, the desired hybrid query result is provided as a response to the hybrid query.

Abstract: Method, system, and programs for hybrid information query. A request is first received from a user associated with a hybrid query. The hybrid query is expressed in accordance with an input in terms of one of a user, a feature, and a document, and a desired hybrid query result in terms of one of a user, a feature, and a document. A mapping is then determined between the input and the desired hybrid query result. A hybrid model is established based on hybrid information collected and associated with one or more users. The mapping is performed based on the hybrid model to obtain the desired hybrid query result based on the input. Eventually, the desired hybrid query result is provided as a response to the hybrid query.

Abstract: One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.

Abstract: One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.

Abstract: Methods, systems, and programming for facilitating users to obtain content are disclosed. In one example, a user is facilitated to create one or more channels each associated with an interest. Content is gathered for each of the one or more channels based on the interest associated with the channel. The content in each of the one or more channels is refined based on an interaction between the user and the channel. At least part of the content in each of the one or more channels is presented to the user. The presented content is determined based on relevance between each piece of content and the associated interest of the channel.

Abstract: Method, system, and programs for online advertising are disclosed. One or more targets associated with an entity are identified based on a first piece of information related to each target and a second piece of information related to the entity. The one or more targets are considered as likely being interested in content that can be made available by the entity. A connection is established between the entity and each identified target through a bi-directional communication channel. Activities between the entity and each target through the bi-directional communication channel are monitored. The entity delivers the content to an identified target through the bi-directional communication channel established between the entity and the identified target. In response to the delivered content, the identified target is able to provide a feedback to the entity through the bi-directional communication channel.

Abstract: Method, system, and programs for recommending content to a user. First information related to one or more previous users is first obtained. A model that maps from users to topics of interest is then established based on the first information related to the one or more previous users. Second information related to the current user is also obtained. One or more topics of interest are identified for the current user based on the model. Content is recommended to the current user in accordance with the one or more topics of interest for the current user. Eventually, an updated model is generated by integrating information associated with the current user with the model established based on the first information related to the one or more previous users. The information associated with the current user includes the second information related to the current user.

Abstract: Performing an on-line recommendation includes: analyzing real-time data from various sources; determining, from the analysis, events in which a user may be interested; extracting the determined events; storing the extracted events in a data store; and performing a recommendation function. The recommendation function includes: ranking the extracted events to determine the events in which the user is most likely to be interested; and performing location-based filtering, retaining those extracted events that are within a geo-location range proximate to the user, thus generating optimal events.