Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for changing a classification of a landing page, such as via, for example, identifying features of the landing page, such as to predict a binary classification of the landing page as to post-click user experience. One or more adjustments to features of the landing page may be determined, such as using a machine learning approach, by way of non-limiting example.

Abstract: A filtering machine receives sponsored content and filters the sponsored content according to a quality metric generated by quality model circuitry and assigned to the instance of sponsored content. The quality model circuitry generates the quality metric in accordance with historical feedback received about other sponsored content and a collection of quality factors pertaining to the sponsored content. Based on the quality metric for the sponsored content, the filtering machine can effect service of the sponsored content to a user device for display thereon.

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 present teaching relates to providing content supply adjustment. A first dataset associated with user interactions directed to one or more content items placed on a target property and a second dataset associated with user interactions directed to the one or more content items places on a reference property are received for evaluation. Further, a cost of placing the one or more content items on the target property is determined based on the first and second datasets.

Abstract: The present teaching relates to analyzing user behavior associated with web contents. Information related to user interactions associated with a content item placed on a reference property is first obtained. A measurement associated with each user interaction of the content item is determined based on the obtained information. An analyzing model for the content item which characterizes statistics of the measurements associated with the content item is further constructed. A measurement threshold to be used to determine a cost of placing the content item on a target property is further determined using the constructed analyzing model.

Abstract: Many content systems (e.g., social networks) present to a user a set of content items posted by other individuals. The user may selectively view content items that reinforce and are consistent with the user's perspective, creating an “echo chamber” effect. Conversely, content systems that selectively expose users to content items exhibiting contrary perspectives, and from individuals with no connection with the user, may alienate the user. Presented herein are techniques for recommending content items that present a different perspective from that of the user, and from individuals who share a similar profile to the user (e.g., alternative opinions from other individuals within the user's social circle or community). Optionally, opinions may be selected that do not directly oppose the user's perspective, but that are orthogonal with it. Such selective recommendations may persuade the user to consider contrary viewpoints that may alter the user's perspective while reducing user alienation.