Methods, apparatus, systems and articles of manufacture are disclosed to identify non-traditional asset-bundles for purchasing groups using social media. An example method includes identifying an asset-bundle in a social media message and generating a profile for a user associated with the social media message. The example method also includes identifying a plurality of social media messages posted by cohorts of the user based on the generated profile and classifying the asset-bundle based on occurrences of the asset-bundle in the plurality of social media messages

Systems and methods for producing, delivering, and managing leads, that automatically create lead collectors on arbitrary websites and independently of marketers are disclosed. The leads that are collected by the system from arbitrary websites can be sold to marketers which were not specified prior to the collection of the leads. The lead collectors are generated according to the design and content of a given website and the information known about the person currently viewing it.

The present teaching relates to method, system, and medium for determining audience clusters to be assigned to an advertisement exposure opportunity. A plurality of advertisement exposure opportunities are obtained, wherein each of the plurality of advertisement exposure opportunities is to be used for placing an advertisement. For each of the plurality of advertisement exposure opportunities, a plurality of audience clusters are generated based on at least one criterion associated with the advertisement exposure opportunity. The plurality of audience clusters are ranked based on at least one ranking criterion, and ordered audience clusters are generated based on the ranking. The ordered audience clusters associated with the plurality of advertisement exposure opportunities in response to an event are updated, wherein the ordered audience clusters for each of the plurality of advertisement exposure opportunities are generated prior to receiving a request for placing the advertisement.

Examples relate to grouping students using content fields. Student data including a plurality of content fields is obtained. Each content field of the plurality of content fields includes a value that represents an unstructured marking linked to a content data collection. Student profiles are generated by assigning a student identification number to each of the plurality of content fields. Each of the student identification numbers are organized into at least one student group by analyzing the set of student profiles.

Methods, systems, and storage media for identifying a sequence of events and participants for record objects are disclosed. Exemplary implementations may: access record objects of a system of record; identify a subset of record objects associated with a group entity and having a first record object status; identify one or more electronic activities linked to the record objects; determine an event-participant pattern based on the electronic activities linked to the record object; identify electronic activities linked with a second record object; determine that a first event is performed by the a participant type and a second event is not yet performed by a second participant type; generate a content item identifying an action to trigger a performance of the second event; and transmit the content item to a device of a participant of at least one electronic activity linked with the second record object.

A method for clustering geolocations using geo-point density includes receiving a user log of geolocation data extracted from user interactions with at least one electronic device. A density is determined relative to other geo-points for each geo-point in a set of geo-points extracted from the user log. Lower density geo-points in the set are merged into higher density geo-points in the set to result in a merged set of geo-points, and clusters of geo-points are identified from the merged set. Merging the geo-points tends to preserve frequently occurring geo-points while reducing those that constitute noise, which improves the reliability of identifying the clusters. Core geo-points of the user log are selected from the clusters. The core geo-points of the user log can be compared to core geo-points of other use logs to identify associations between the user logs.

An enhanced information retrieval system takes a customer utterance and constructs a contextually-enriched content-based query allowing the system to retrieve the most relevant documents to assist an agent in a real-time conversation with the customer. Phrases in the utterance are classified as informational or non-informational using a machine learning system trained with phrases from prior conversations of multiple users. Content phrases are extracted from the informational phrases using keyword extraction (ranking noun phrases), intent/action extraction (semantic role labeling), and topic label extraction (clustering of historical logs). Emotional content is identified using a sequence tagging model and removed. Contextual information from prior conversations with this user is combined with the updated content phrases to create the contextually-enhanced content-based query, which can then be submitted to the information retrieval system.

There is provided a device and method of information processing, and a program, capable of displaying a candidate selected by incorporating a user's intention, relating to received information. The information processing device including: a selection information acquisition unit configured to acquire partial information selected by a first user from first information received from another device; and a display control unit configured to generate display control information used to display a selection candidate relating to the first information on the basis of the acquired partial information.

Author identification based on functional summarization is disclosed. One example is a system including a plurality of summarization engines, each summarization engine to receive, via a processing system, a text content to provide a summary of the text content. At least one meta-algorithmic pattern is applied to at least two summaries to provide a meta-summary of the text content using the at least two summaries. A content processor identifies, from the meta-summary, authorship features associated with the text content. An evaluator determines, for a given category, a category value of the meta-summary, the category value indicative of a similarity of the authorship features to the category. A selector selects, for the given category, a combination of meta-algorithmic patterns and summarization engines that provides the meta-summary that optimizes the category value for the text content.

A computer system for parsing bulk message information into intelligent question retrieval models receives text-based data associated with a particular user. The system encodes the word into a context-dependent vector, wherein the context-dependent vector indicates the meaning of the word across a semantic space. The system also identifies within a context-independent database a context-independent vector that is associated with the word. Further, the system generates an objective output by combining the context-dependent vector and the context-independent vector. Further still, the system generates a sentence encoding representation by processing at least a portion of the text-based data through a high-level feature embedded convolutional semantic model to generate numerical representations of questions and answers within the text-based dataset. The sentence encoding representation is generated at least in part based upon the objective output.