Abstract:
The present teaching, which includes methods, systems and computer-readable media, relates to detecting online coalition fraud. The disclosed techniques may include grouping visitors that interact with online content into clusters, obtaining traffic features for each visitor, wherein the traffic features are based at least on data representing the corresponding visitor's interaction with the online content; determining, for each cluster, cluster metrics based on (one or more statistical values of) the traffic features of the visitors in that cluster; and determining whether a cluster is fraudulent based on the cluster metrics of the first cluster. For example, determining whether a cluster is fraudulent may include determining whether a first statistical value of the traffic features related to the first cluster is greater than a first threshold value, and/or determining whether a second statistical value of the traffic features related to the first cluster is lower than a second threshold value.
Abstract:
Method, system, and programs for determining a keyword from user agent strings are disclosed. In one example, a plurality of user agent strings is received. The plurality of user agent strings is grouped into one or more clusters. The one or more clusters comprise a first cluster that includes two or more user agent strings. The two or more user agent strings in the first cluster are compared. Based on the comparing, a keyword is determined from the first cluster. The keyword represents a type of user agent information.
Abstract:
A system and method generates and recommends a short and pleasant path between a source s and destination d in a geo-location such as a city or city center. The routes are not only short but emotionally pleasant, offering an engaging user experience, going beyond just showing paths on a map.
Abstract:
Briefly, for an embodiment, as an example, a method may include recovering one or more cookies for a particular user based at least in part on one or more cookie maps.
Abstract:
Techniques for creating a social network are provided. Private relationships that are established (e.g., in the context of instant messaging) may become public by the action of a single user. Each user determines whether they want to be “social” to (or discoverable by) friends of the user's friends. For example, user A is a friend of (i.e., has established a relationship with) user B and user B is a friend of user C, but user A and user C are not friends of each other (i.e., user A and user C have not established a relationship with each other). If user C unilaterally takes an action, then user A is able to see that user C is a friend of user B. User A may then take further actions to attempt to establish a relationship with user C or otherwise contact user C.
Abstract:
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.
Abstract:
In one embodiment, a location of a mobile device may be obtained. A direction of movement of the mobile device may be ascertained. A field of vision of a user of the mobile device may be determined based, at least in part, on the location of the mobile device and the direction of movement of the mobile device. A user profile associated with the user and/or the mobile device may be identified. A notification may be provided via the mobile device based, at least in part, upon the user profile and the field of vision of the user.
Abstract:
The technologies described herein identify multiple electronic devices belonging to the same group. A computer system receives, from network accessing applications of a plurality of electronic devices, internet protocol (IP) trajectory information about the network accessing applications via a network. The IP trajectory information includes a user identifier, a list of IP addresses associated with each of the network accessing applications, and timestamps specifying times each of the network accessing applications accesses the network. The computer system identifies and removes commercial IP addresses from the list of IP addresses, analyzes IP trajectory information to retrieve a most commonly used IP address for each of the network accessing applications during a certain period, and determines that different network accessing applications belong to the same group if the most commonly used IP addresses for the different network accessing applications are the same.
Abstract:
A content item categorizer system retrieves content items from Internet sources. If a retrieved content item includes sufficient information for traditional categorization methods, then the system assigns one or more categories to the content item using such traditional methods. The system creates a metadata model, based on information about traditionally-categorized content items, that maps at least hashtags from the content items to one or more content categories. When the system retrieves a sparse-info item that does not include sufficient information for traditional categorization, the system applies the metadata model to categorize the content item using at least hashtags in the sparse-info item. The metadata model may also include information indicating mappings between categories and coincidence of hashtags and additional content item attributes. Also, the metadata model may provide information for categorizing sparse-info items based on multiple hashtags in the sparse-info item metadata.
Abstract:
Automated systems and methods are provided for establishing or maintaining a personalized trusted social network for a community of users, with little or no input from any given user. To establish the personalized trusted social network, one or more trusted sources are identified for a given user. The identified trusted sources are added to a user profile for the given user. Also, identified are any annotations, bookmarks, or the like that the identified trusted sources have associated with any shared content. These annotations provide access to microcontent items that the identified trusted sources have integrated with the shared content to thereby enhance or enrich its context. One or more profiles are constructed or updated to track the associations between the identified trusted sources and their annotations. The profile information can be applied to enhance and personalize search and browsing experiences for the given user.