Yahoo! Inc.

Excalibur Ip, LLC

Verizon Patent and Licensing Inc.

Oath Inc.

Verizon Media Inc.

Yahoo Holdings, Inc.

Francesco Bonchi

Nicola Barbieri

Aristides Gionis

Michele Berlingerio

Daniele Quercia

Carmen Vaca

Giuseppe Manco

Björn Bringmann

Debora Donato

Yoelle Smadja Maarek

Liang-Yu Tom Chi

Ilaria Bordino

Gianmarco De Francisci Morales

Systems and methods for discovering and annotating geo-fences from geo-referenced data are disclosed. The systems and methods input an area of interest containing a plurality of geo-referenced points having associated labels, and divides the area interest into cells. Each cell is assigned an initial label from among the plurality of labels and hierarchical clustering is used to find clusters of cells having a common label based on a maximization of an objective function for each cell with the objective function being dependent upon favoring spatially adjacent cells having a common label and limiting overgeneralization of the common label.

Taxonomy-based system for discovering and annotating geofences from geo-referenced data

Provided herein is a system or method for a users-to-follow recommendation engine for, based at least in part on social network information and information about users in one or more social networks, determining features relating to users, including topical features and social features, determining, using a model constructed utilizing the determined features, for a set or users, a subset of the set of users for which the user has a high linkage, relative to other linkages in the set, and determining, using the model, and displaying to the user, a recommendation to follow and an associated explanation, of at least one particular user of the subset of the users wherein the associated explanation includes a topical-based explanation when a predominant basis for the high linkage is determined to be topical and a social-based explanation when a predominant basis for the high linkage is determined to be social.

Systems and methods for providing recommendations and explanations

The disclosure includes use of a feature-aware propagation model to identify one or more features of a product and one or more person(s), or members of a social network, to target, or user, for marketing the product having the identified features. The one or more person(s) identified using the model may be the person(s), or member(s), of a social network determined to have a maximum capability, relative to other members of the social network, for influencing the members of the social network in adopting, e.g., purchasing, a product having the identified features. In addition, parameters of the model may be determined using information about the social network, user preferences, and the products and features of the products.

Influence maximization with viral product design

Disclosed are methods and apparatus for selecting items (e.g., Internet memes) to be presented to a user. In one embodiment, a user connected to a network is identified. For each of a plurality of items (e.g., memes), for each of one or more followers of the user, a probability that the corresponding one of the followers of the user will repost the one of the plurality of items is determined. A number of the plurality of items may be selected based, at least in part, on the probability that has been determined, for each of the plurality of items, for each of the one or more followers of the user, that the corresponding one of the followers of the user will repost the one of the plurality of items. The selected number of the plurality of items may then be presented to the user.

System for ranking memes

A network's evolution is characterized by graph evolution rules. A graph, formed by merging multiple graphs representing the multiple snapshots of the network, that represents an evolutionary network is mined to identify evolutional patterns of the network. A pattern is selected from the identified patterns. Graph evolution rules are generated using identified evolutional patterns. The generated graph evolution rules represent the evolutional patterns of the network, the rules indicating that any occurrence of a child pattern of the selected pattern implies a corresponding occurrence of the selected pattern.

Network graph evolution rule generation

Methods and systems for identifying communities based on information propagation data are described. One of the methods includes receiving a social graph, which includes nodes and relationships between the nodes. The method further includes receiving a number of the communities to find within the social graph, receiving data regarding propagation of information between the nodes, and calculating a probability of formation of a link between a first one of the nodes and a second one of the nodes based on the data. The link provides a direction of flow of media between the first and second nodes. The method includes calculating a probability that media will be accessed by the second node based on the data. One of the communities includes the first node, the second node, and the link.

Identifying communities within a social network based on information propagation data

Disclosed is a system and method for recommending web content to a second screen. A server computer analyzes closed captioning text associated with a media program being experienced in a time period by a user having a client device. In response to the analyzing of the closed captioning text, the server automatically extracts an entity from the closed captioning text and determines an online web page describing the entity. The server utilizes a graph model to retrieve a relevant information item for the entity, where the graph model includes a plurality of entity nodes and a plurality of query nodes. The server communicates, within the time period, the relevant information item to the client device.

Method and system for recommending relevant web content to second screen application users

A system and method is described herein that automatically determines if a user of a search engine is conducting a research mission and then provides one or more research tools, one or more specialized searches, one or more directed ads, and/or one or more marketplace events responsive to determining that the research mission is being conducted. The automatic provision of various events and/or tools responsive to determination of the research mission can advantageously improve the experience of the user conducting the research mission.

Research mission identification

Methods and apparatus are described for modeling sequences of events with Markov models whose states correspond to nodes in a provided taxonomy. Each state represents the events in the subtree under the corresponding node. By lumping observed events into states that correspond to internal nodes in the taxonomy, more compact models are achieved that are easier to understand and visualize, at the expense of a decrease in the data likelihood. The decision for selecting the best model is taken on the basis of two competing goals: maximizing the data likelihood, while minimizing the model complexity (i.e., the number of states).

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Barcelona, Spain

Francesco Bonchi