Abstract:
The present teaching relates to ranking search content. In one example, a plurality of documents is received to be ranked with respect to a query. Features are extracted from the query and the plurality of documents. The plurality of documents is ranked based on a ranking model and the extracted features. The ranking model is derived to remove one or more documents from the plurality of documents that are less relevant to the query and order remaining documents based on their relevance to the query. The ordered remaining documents are provided as a search result with respect to the query.
Abstract:
Method, system, and programs for identifying a target metric. In one example, at least one first type of metric computed based on a first period associated with a first length of time is measured for each of a plurality of users. At least one second type of metric computed based on a second period associated with a second length of time is measured for each of the plurality of users. The second length of time is larger than the first length of time. Correlations between each of the at least one first type of metric and each of the at least one second type of metrics are computed with respect to the plurality of users. A target metric is identified from the at least one first type of metric based on the correlations. The target metric is correlated with the at least one second type of metric.
Abstract:
Method, system, and programs for measuring user engagement. In one example, a type of user activities with respect to a plurality pieces of content in a content stream is detected. A first depth measure with respect to the content stream is obtained based on the detected type of user activities. A function is generated based on the first depth measure and a second depth measure with respect to the content stream. A user engagement score is calculated for each of the plurality pieces of content in the content stream based on the function.
Abstract:
A method and system for exploring a list of user interests beyond the currently known user interests by defining a distance metrics in the interest space is disclosed. The new method and system target for exploration, items of interests which are close in proximity to the current set of user interests, thereby greatly improving the chance that one of the exploration items will be liked by the user.
Abstract:
Embodiments of the present teachings disclose method, system, and programs for a multi-phase ranking system for implementation with a personalized content system. The disclosed method, system, and programs utilize a weighted AND system to compute a dot product of the user profile and a content profile in a first phase, a content quality indicator in the second phase and a rules filter in a third phase.
Abstract:
A server system of an online information system displays advertising items and content items retrieved from storage devices as a stream viewable by a user on a user device. The advertisement items and the content items are ordered in the stream by a ranking score computed for each of the advertisement items and each of the content items. The server system transmits a web page including the stream to a user device over a network. In this manner, advertising items and content items compete in a unified marketplace for inclusion in the stream for viewing by the end user.
Abstract:
Methods, systems, and computer programs are presented for selecting news articles for presentation to a user. One method includes an operation for measuring dwelltimes for a first set of news items, where the dwelltime for a news item is based on the amount of time that the news item is displayed to a viewer. Further, the method includes an operation for training a classifier of news items based on the measured dwelltimes and based on features associated with the first set of news items. Additionally, the method includes an operation for ranking with the classifier a second set of news items for presentation to the user, the ranking also using the profile of the user for delivery of customized news to the user. The ranked second set of news item is then presented to the user.
Abstract:
A server system of an online information system displays advertising items and content items retrieved from storage devices as a stream viewable by a user on a user device. The advertisement items and the content items are ordered in the stream by a ranking score computed for each of the advertisement items and each of the content items. A quality scoring system determines an affinity score between a user and a present content item based on features of the present content item matching user profile parameters associated with the user and identifies post-interaction satisfaction with a prior content item. The quality scoring system determines a quality score based on the affinity score and the post-interaction satisfaction. The quality score is used for ordering items in the stream. The server system transmits a web page including the stream to a user device over a network. In this manner, advertising items and content items compete in a unified marketplace for inclusion in the stream for viewing by the end user.
Abstract:
The present teaching relates to rewriting a query and providing search results. In one example, a plurality of queries is obtained. For each of the plurality of queries, one or more search results are identified. The one or more search results have been obtained in response to the query and have been previously selected by a user submitting the query. A plurality of titles is obtained. Each of the titles corresponds to one of the one or more search results with respect to one of the plurality of queries. A model is generated based on the plurality of queries and the plurality of titles. The model is to be used for rewriting a query.
Abstract:
A server system of an online information system displays advertising items and content items retrieved from storage devices as a stream viewable by a user on a user device. The advertisement items and the content items are ordered in the stream by a ranking score computed for each of the advertisement items and each of the content items. A quality scoring system determines an affinity score between a user and a present content item based on features of the present content item matching user profile parameters associated with the user and identifies post-interaction satisfaction with a prior content item. The quality scoring system determines a quality score based on the affinity score and the post-interaction satisfaction. The quality score is used for ordering items in the stream. The server system transmits a web page including the stream to a user device over a network. In this manner, advertising items and content items compete in a unified marketplace for inclusion in the stream for viewing by the end user.