Yahoo! Inc.

Facebook, Inc.

Meta Platforms, Inc.

Yahoo Holdings, Inc.

Pradheep K Elango

Deepak Agarwal

Bee-Chung Chen

Nitin Motgi

Shyamsundar Rajaram

Wei Chu

Raghu Ramakrishnan

H Scott Roy

Raghunath Ramakrishnan

Zheng Chen

John Thrall

Amitabh Seth

Howard Scott Roy

Vik Singh

Vijay Krishna Narayanan

Seung-Taek Park

Xian Xu

Joe Zachariah

Sharat Israni

Chandar Venkataraman

Sameer Indarapu

Amit Phadke

Michael Salisbury

Stephen O'Sullivan

An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.

Conversion optimization with long attribution window

Embodiments are disclosed for predicting target events occurrence for an advertisement campaign. A computing device according to some embodiments assigns a label to an advertisement as unlabeled, in response to a notification that a prerequisite event occurs for the advertisement. The device generates feature vectors based on data that relate to the advertisement. The device further trains a machine learning model using the feature vectors of the unlabeled advertisement based on a first term of an objective function, without waiting for a target event for the advertisement to occur. The first term depends on unlabeled advertisements. The device predicts a probability of a target event occurring for a new advertisement, by feeding data of the new advertisement to the trained machine learning model.

Advertisement conversion prediction based on unlabeled data

A web site receives, by a computer, a request from a user device to display a news item web page. The computer determines a time and date that the user previously visited the web site to view news items, identifies news items published after the determined time and date, dynamically generates a web page including at least a portion of the identified news items, and transmits the generated web page to the user device.

Missed media system and method

Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

Enhanced matching through explore/exploit schemes

Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a 'controlled' manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.

Framework to evaluate content display policies

Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.

Customized today module

The present invention provides for the generation of structured query results using lexical clustering which includes collecting one or more search queries and data associated with the one or more search queries. The present invention further includes preprocessing the one or more queries into a canonicalized form of each of the one or more queries. The canonicalized form of each of the one or more queries may be accomplished using stemming, punctuation, pluralization, word order or other canonicalization rules. The present invention further includes building a lexical index of the one or more search queries and data associated with the one or more search queries and mining the lexical index of the one or more search queries and data associated with the one or more search queries in order to generate a structured query result set.

Method for generating structured query results using lexical clustering

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Mountain View, CA, United States of America

Pradheep K Elango