Yahoo Assets LLC

Oath Inc.

Verizon Media Inc.

Rao Shen

Donghyun Kim

Kostas Tsioutsiouliklis

Yufeng Ma

Liuqing Li

Yu Wang

Akshay Soni

Troy Chevalier

Xue Wu

Parikshit Shah

Pierce Yang

Chang Lu

Yue Ning

Xinyue Wang

In an example, in response to a request for content, content items may be identified. Content item scores associated with the content items may be determined using a content scoring model. A first ranked list may be generated based upon the content item scores using an exploration model. The first ranked list is indicative of rankings of the content items. A content item selection interface may be generated based upon the first ranked list. The content item selection interface may include selectable objects, corresponding to the content items, arranged according to rankings indicated by the first ranked list.

System and method for ranking content items

In an example, in connection with a search clustering system, a grouping component retrieves a timestamp set of news queries and determines a time-stable set of news query groups by performing the first stage of a two-stage clustering technique. A clustering component determines a time-stable set of news query groups clusters by performing the second stage of the two-stage clustering technique. The performance of the two-stage clustering technique is aided by a least recently used caching component. The time-stable set of news query groups clusters may be served to a web page in order to generate a trending topic list for display.

Two-stage time-enriched system and method for query clustering

The disclosed systems and methods provide a novel framework that provides mechanisms for a Deep & Cross Network (DCN) framework that performs distilled deep prediction for personalized stream ranking on portal websites. The disclosed framework is scalable to satisfy the much more stringent latency and computational requirements required by current network operating environments. The disclosed framework is able to dynamically evaluate and leverage live traffic on network sites in order to provide, update and maintain current recommendations for users as they traverse to a portal and when they navigate within the portal. The disclosed framework implements a DCN model(s) that is capable of being compressed into a model size for a unified optimization within a live traffic environment by combining knowledge distillation and model compression techniques. The disclosed framework is built as a light-weight deep learning model that can be served in production and perform on par with large models.

Computerized system and method for distilled deep prediction for personalized stream ranking

One or more computing devices, systems, and/or methods for debiasing training data based upon information seeking behaviors are provided. Users associated with a set of training data are segmented into information seeking behavior groups corresponding to varying degrees of information seeking behaviors of the users. Biases for the information seeking behavior groups may be estimated based upon information seeking behaviors of users within the information seeking behavior groups. The training set of data is debiased using the biases to generate a debiased training set of data. A model may be trained to perform a task based upon the debiased training set of data.

Debiasing training data based upon information seeking behaviors

In an example, first entities are extracted from user profiles. Second entities are extracted from content information associated with content item. User-associated metrics associated with the first entities are determined based upon the user profiles and/or content events. First vector representations of the first entities and second vector representations of the second entities are processed to generate an attention distribution array. Each value of the attention distribution array represents, for a user interested in an entity of the first entities, a proportion of (i) entity-specific activity, of the user, related to an entity of the second entities relative to (ii) an entirety of activity of the user. An inferred activity distribution array is generated by applying the user-associated metrics to the attention distribution array. A filtered subset of activity distribution values is generated by pruning values from the inferred activity distribution array. Transmission of content is controlled using the filtered subset of activity distribution values.

System and method for feature determination and content selection

The present teaching relates to personalized content recommendation. A webpage is contrasted for a user having a plurality of slots each of which is to be allocated with a content item. For each of the plurality of slots, a plurality of content items in a plurality of types of content are accessed. For each of the plurality of types of content, a personalized score is predicted for each content item in the type of content, wherein the personalized score is obtained based on a trained model trained. A recommended content item of the type of content is selected based on personalized scores. An overall recommended content item is selected and allocated to a slot based on criteria associated with the personalized scores of the recommended content items and a business rule. The webpage with the plurality of slots allocated with content items is provided to the user.

Method and system of personalized blending for content recommendation

Systems and methods are disclosed for discovering precursors associated with a current user interaction event. One method comprises receiving a selection of a new item by a user and determining a plurality of similarities between the new item selected by the user and a plurality of historical items, the plurality of historical items being associated with prior user activity. Then a plurality of importance weights associated with the plurality of historical items are determined. Based on the plurality of similarities and the plurality of importance weights, at least one enhanced importance matrix is generated. Further, prior interactions of the user with the plurality of historical items are determined. Based on the enhanced importance matrix and the prior interactions of the user with the plurality of historical items, precursors for the new item selected by the user are identified and provided to a display.

Analytical precursor mining for personalized recommendation

A method is provided, including: detecting interactions by a plurality of users with a plurality of content items, each content item having an associated content item vector; for a given user, identifying interactions occurring during a current time period, including identifying positive interactions with a first set of the content items, and negative interactions with a second set of the content items; processing a first set of the content item vectors that are associated with the first set of the content items to determine a positive interaction vector; processing a second set of the content item vectors that are associated to the second set of the content items to determine a negative interaction vector; for the given user, generating a current user profile vector for the current time period, using the positive interaction vector, the negative interaction vector, and a prior user profile vector for a prior time period.

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Sunnyvale, CA, United States of America

Rao Shen