摘要:
An ad matching system that includes an interactive client permits a triggering Web page author to provide feedback on a candidate advertisement for the page. Author feedback is used to rank ads for display on the triggering page. Preferably author feedback is also incorporated into ad clustering and/or ad ranking formulae within the system. Also, author credibility is judged based on author feedback and on click through rates of placed ads.
摘要:
Systems and methods for identifying spam hosts are disclosed in which hosts known to the system and initially classified as spam or non-spam by a baseline classifier. Then for each node u in the host graph a new feature is computed. This feature is an aggregate function of the initial classifications produced by the baseline classifier for the neighbors of the node u. The set of neighbors can be defined in many different ways: in-link neighbors, out-link neighbors, bi-directional neighbors, k-hops neighbors, etc. The new feature computed above then is added to the existing set of features, and the baseline classifier is trained again, producing new predictions for each node. The results may then be used in many different ways including to filter search results based on host classifications so that spam hosts are not displayed or displayed last in a results set.
摘要:
Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate or otherwise support one or more processes or operations for identifying points of interest in a text, such as in an unstructured text, for example, in connection with bootstrapping points of interest via social media.
摘要:
A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.
摘要:
An ad matching system that includes an interactive client permits a triggering Web page author to provide feedback on a candidate advertisement for the page. Author feedback is used to rank ads for display on the triggering page. Preferably author feedback is also incorporated into ad clustering and/or ad ranking formulae within the system. Also, author credibility is judged based on author feedback and on click through rates of placed ads.
摘要:
Exemplary methods and apparatuses are disclosed that may be used to provide or otherwise support extraction of objects and facets from one or more extraction corpora and ranking of said facets using multiple ranking corpora.
摘要:
Exemplary methods and apparatuses are disclosed that may be used to provide or otherwise support ranking entity relations utilizing the vocabulary of at least one external corpus for use in search engine information management systems.
摘要:
A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.