International Business Machines Corporation

Google Inc.

Jilin Chen

Jalal U Mahmud

Lichan Hong

Ed Huai-Hsin Chi

Huazhong Ning

Tianjiao Zhang

Peng Dai

Michelle Xue Zhou

Jeffrey William Nichols

Sagar Jain

Amy Xian Zhang

Kyumin Lee

Huiji Gao

Implementations provide an improved system for presenting search results based on entity associations of the search items. An example method includes generating first-level clusters of items responsive to a query, each cluster representing an entity in a knowledge base and including items mapped to the entity, merging the first-level clusters based on entity ontology relationships, applying hierarchical clustering to the merged clusters, producing final clusters, and initiating display of the items according to the final clusters. Another example method includes generating first-level clusters from items responsive to a query, each cluster representing an entity in a knowledge base and including items mapped to the entity, producing final clusters by merging the first-level clusters based on an entity ontology and an embedding space that is generated from an embedding model that uses the mapping, and initiating display of the items responsive to the query according to the final clusters.

Clustering search results

Embodiments relate to user attitude modeling and behavior prediction for a social media network. One aspect includes collecting data relating to previously demonstrated sentiments, opinions, and actions attributed to network users toward a topic. Another aspect includes creating a model from the data, which includes factorizing the actions for behavior inference, factorizing auxiliary content from the network for opinion and sentiment inferences, and applying sentiment and opinion regularization to constrain user preferences on implicit topics to explicit sentiments and explicit opinions. Another aspect includes applying the model to a new user of the network with respect to the topic, and generating a prediction with respect to the user that includes predicting sentiment and opinion as a function of the auxiliary content and feature coefficients learned during a training process, and predicting a future action of the user as a function of the auxiliary content and latent profiles of the topic.

Modeling user attitudes toward a target from social media

Implementations provide an improved system for presenting search results based on entity associations of the search items. An example method includes, for each of a plurality of crowdsource workers, initiating display of a first randomly selected cluster set from a plurality of cluster sets to the crowdsource worker. Each cluster set represents a different clustering algorithm applied to a set of search items responsive to a query. The method also includes receiving cluster ratings for the first cluster set from the crowdsource worker and calculating a cluster set score for the first cluster set based on the cluster ratings. This is repeated for remaining cluster sets in the plurality of cluster sets. The method also includes storing a cluster set definition for a highest scoring cluster set, associating the cluster set definition with the query, and using the definition to display search items responsive to the query.

Crowdsourced evaluation and refinement of search clusters

A computer-implemented method, computer program product, and computer system for selecting strangers for information spreading on a social network. For the strangers who are users of the social network and not related to each other, information spreading probabilities based on features, information reach, and information spreading probabilities based on a wait time are computed. Fitness scores of the strangers are computed; the fitness scores are a function of the information spreading probabilities, the information reach, and the information spreading probabilities. The strangers are ranked, based on the fitness scores, in a sorted set. One or more of the strangers for the information spreading are selected from the sorted set. The one or more of the strangers for the information spreading are selected by determining an interval in the sorted set, and the interval satisfies an optimization objective of minimizing time for the information spreading within a confidence probability.

Selecting strangers for information spreading on a social network

Embodiments of the invention relate to predicting user activity in a social media application. In one embodiment, user activity information is collected for a user from a social media application and activity features of the user are determined, based on the collected activity information for the user. Then a model is created to predict a future activity of the user in the social media application. The model uses the determined activity features of the user and results obtained from running the created model, to determine future activity of the user in the social media application.

Predicting user activity in social media applications

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Sunnyvale, CA, United States of America

Jilin Chen