Abstract: A sheet packaging material for producing a sealed package containing a food product, comprising crease lines that have sections that are angled towards each other such that at least two sides panels of the sealed package are given a trapezoidal shape.

Abstract: 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.

Abstract: An online advertising system receives an advertisement from an advertiser. The system analyzes the advertisement, extracts its features and provides to the advertiser a quality rating for the advertisement which depends on a user engagement factor such as the predicted dwell time for the ad, given its features. The system further provides to the advertiser suggestions for improvements to the advertisement, such as a list of actionable guidelines that can improve the expected dwell time of the ad, and likely its conversion rate.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein is a matching of multiple different social graphs to generate a combined social graph. Such a combined social graph may be searched and used in determining information to provide to a user, for example. An iterative metric learning approach may be used in matching multiple different social graphs. A mechanism is provided to validate a match from different social graphs. Match validation of data field matching is provided.

Abstract: Methods and/or systems are provided that may be utilized to recommend a contextual walking route for a number of venues, for example.

Abstract: An online advertising system receives an advertisement from an advertiser. The system analyzes the advertisement, extracts its features and provides to the advertiser a quality rating for the advertisement which depends on a user engagement factor such as the predicted dwell time for the ad, given its features. The system further provides to the advertiser suggestions for improvements to the advertisement, such as a list of actionable guidelines that can improve the expected dwell time of the ad, and likely its conversion rate.

Abstract: 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.

Abstract: 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.

Abstract: Methods and/or systems are provided that may be utilized to recommend a contextual walking route for a number of venues, for example.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein is a matching of multiple different social graphs to generate a combined social graph. Such a combined social graph may be searched and used in determining information to provide to a user, for example. An iterative metric learning approach may be used in matching multiple different social graphs. A mechanism is provided to validate a match from different social graphs. Match validation of data field matching is provided.

Abstract: Disclosed is a system and method for detecting online social communities through network-oblivious community detection techniques that involve modeling social contagion from a log of user activity. The log includes a dataset of tuples that record the instances when a user has adopted an item at a specific time. The disclose systems and methods then apply a stochastic framework that assumes that the adoptions of the item are governed by an underlying diffusion process over an unobserved social network, and that such diffusion model is based on community-level influence. By fitting the model parameters to the user activity log, community membership information and level of influence information can be derived for each user in each community.

Abstract: 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.