Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. Based on likelihoods of the user accessing content items including different candidate products, the online system selects a candidate product and includes the content item having information about the selected candidate product in one or more selection processes that select content for presentation to the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. The online system may include differing levels of information about a selected candidate product in the content item. In various embodiments, the online system determines a level of information about the selected candidate product based on products previously accessed by the user.

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about content provided by one or more third party systems accessed by the user and determines products associated with accessed content. When the online system identifies an opportunity to present to a user, the online system identifies products for inclusion in the content item and identifies candidate products for inclusion in the content item based on products previously accessed by the user. The online system selects a product of the candidate products based on probabilities of the user accessing content items including different candidate products. The online system includes the content item having information about the selected product in one or more selection processes that select content for presentation to the user.

Abstract: An example of a method includes determining features of a first type for a web page of a plurality of web pages. The method also includes electronically determining a plurality of rules for an attribute of the first web page, wherein the plurality of rules are determined based on features of the first type. The method also includes electronically identifying a first rule, from the plurality of rules, which satisfies a first predefined criterion. The first predefined criteria include at least one of a first threshold for a precision parameter, a second threshold for a support parameter, a third threshold for a distance parameter and a fourth threshold for a recall parameter. The method further includes storing the first rule to enable extraction of value of the attribute from a second web page.

Abstract: Subject matter disclosed herein may relate to clustering electronic documents, such as, for example, web pages, and may also relate to template identification for electronic documents.

Abstract: An example of a method includes determining features of a first type for a web page of a plurality of web pages. The method also includes electronically determining a plurality of rules for an attribute of the first web page, wherein the plurality of rules are determined based on features of the first type. The method also includes electronically identifying a first rule, from the plurality of rules, which satisfies a first predefined criterion. The first predefined criteria include at least one of a first threshold for a precision parameter, a second threshold for a support parameter, a third threshold for a distance parameter and a fourth threshold for a recall parameter. The method further includes storing the first rule to enable extraction of value of the attribute from a second web page.

Abstract: Techniques are disclosed herein to automatically learn a template that describes a common structure present in documents in a training set. The structure of the template is compared to the structure of the documents (or at least a part of each document) in the training set, one-by-one, and generalized in response to differences between the template and the document to which the template is currently being compared. If the structure of any particular document is considered too dissimilar from the structure of the template, then the template is not modified. Various generalization operators are added to the template to generalize the template. One such generalization operator is an “OR”, which indicates that only one of “n” sub-trees below the “OR” operator in the template is allowed at the corresponding position in a document.

Abstract: An example of a method includes generating an attributed extensible markup language path (XPath) for an annotated entity in a web page. The method further includes determining a first node that satisfy the attributed XPath in the web page and is annotated. The method also includes identifying an attribute property that satisfies predefined criteria in the web page while traversing from the first node to a root node, the attribute property comprising an attribute value and an attribute name. Moreover, the method includes populating the attributed XPath with the attribute property that satisfies predefined criteria. The method also includes filtering the attributed XPath to generate a robust XPath, and extracting content from multiple web pages based on the robust XPath.

Abstract: Techniques for high precision multi entity extraction are provided. A wrapper that represents a generalized structure of a set of training web pages is accessed. The wrapper includes one or more annotations that indicate a set of attributes that are included in each of a plurality of records. Record boundaries are determined based on nodes included in the wrapper, where the record boundaries delimit the plurality of records within any training page of the set of training web pages. The wrapper is modified to include one or more boundary nodes, where the one or more boundary nodes indicate the record boundaries of the plurality of records within the set of training web pages. Multiple records are extracted from a web page, where extracting the multiple records comprises detecting record completions based at least on the wrapper and on a document object model (DOM) representation of the web page.

Abstract: Methods and systems are provided herein that may allow for pertinent information-type(s) of data to be located or otherwise identified within one or more documents, such as, for example, web page documents associated with one or more websites. For example, exemplary methods and systems are provided that may be used to determine if information may be more likely to be of an “informative” type of information or possibly more likely to be of a “noise” type of information.

Abstract: Subject matter disclosed herein may relate to clustering electronic documents, such as, for example, web pages, and may also relate to template identification for electronic documents.

Abstract: Techniques are disclosed herein to automatically learn a template that describes a common structure present in documents in a training set. The structure of the template is compared to the structure of the documents (or at least a part of each document) in the training set, one-by-one, and generalized in response to differences between the template and the document to which the template is currently being compared. If the structure of any particular document is considered too dissimilar from the structure of the template, then the template is not modified. Various generalization operators are added to the template to generalize the template. One such generalization operator is an “OR”, which indicates that only one of “n” sub-trees below the “OR” operator in the template is allowed at the corresponding position in a document.