Yahoo! Inc.

Excalibur IP, LLC

Yahoo Assets LLC

Google Inc.

Oath Inc.

Verizon Media Inc.

Yehuda Arie Koren

Yoelle Smadja Maarek

Ingmar Weber

Ronny Lempel

Oren Shlomo Somekh

Natalie Aizenberg

Andrei Z Broder

Lev Finkelstein

Edo Liberty

Nadav Golbandi

Anirban Dasgupta

Vishwanath Tumkur Ramarao

Idan Szpektor

Mark Risher

Kilian Quirin Weinberger

Yoelle Maarek Smadja

Embodiments are directed towards automatically learning user behavioral patterns when interacting with messages and based on the learned patterns, suggesting one or more predicted actions that a user might take in response to receiving subsequent message. One or more classifiers are trained and employed to predict one or more actions that a user might take in response to receiving the message. In one embodiment, the one or more predicted actions are provided suggested to the user as an action the user might take on the message. Messages may be rank ordered within a given suggested action based on a confidence level of the prediction.

Mining email inboxes for suggesting actions

Disclosure includes a system, method and architecture to monitor media content streams, e.g., streams of digital encoded media data such as without limitation audio and/or video data, from a number of media content streaming providers and identify media content items from those media content items being streamed by the media content streaming providers for inclusion in a personalized media content stream for a user. In a direct streaming approach, the media content items contained in a stream are available while each item is being streamed by the content provider. In a centralized streaming approach, media content items contained in each media content stream that is currently being streamed by content provider are available for a limited window of time, which can be dependent on buffer length.

Method and system of dynamic routing of aggregated online media streams

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating answer terms for scoring answer passages. In one aspect, a method includes accessing resource data describing a set of resources, identifying question phrases in the resources, for each identified question phrase in a resource, selecting in the resource a section of text subsequent to the question phrase as an answer, the answer having a plurality of terms, grouping the question phrases into groups of question phrases, and for each group: generating, from the terms of the answers for each question phrase in the group, answer terms and for each answer term, an answer term weight, and storing the answer terms and answer term weights in association with one or more queries.

Weighted answer terms for scoring answer passages

In accordance with one aspect, methods and apparatus facilitate the filtering of unsolicited bulk electronic mail (email) sent from spammers. A plurality of recipient patterns for a plurality of emails from known spammers is logged. A plurality of recipient patterns for a plurality of emails from known non-spammers is also logged. A probabilistic model for predicting whether an unknown sender identity is a spammer is generated or modified based on the logged recipient patterns for the emails from known spammers and known non-spammers.

Apparatus and methods for classifying senders of unsolicited bulk emails

Media item recommendations, such as music track recommendations, may be made using one or more models generated using data collected from a plurality of media stream sources, such as, for example, Internet radio stations. In an initial, bootstrapping phase, data about media items and media stream playlists of media stream sources may be used to generate a model, which comprises latent factor vectors, or learned profiles, of media items, e.g., tracks, artists, etc. Such a bootstrapping phase may be performed without user data, such as user playlists and/or user feedback, to generate a model that may be used to make media item recommendations. As user data becomes available, e.g., as users of a recommendation service provide user data, the user data may be used to supplement and/or update the model and/or to create user profiles.

Media recommendation using internet media stream modeling

Bootstrapping a recommender system that makes item recommendations. The bootstrapping identifying questions for use in interviewing a user, e.g., a new user to the recommender system, to obtain user information, e.g., user profile information, for use in predicting item ratings for the user, the predicted item ratings for use in identifying item recommendations for the user. The bootstrapping using a cost function to minimize error in selection of the questions for the interview. The bootstrapping comprising a static bootstrapping and an adaptive bootstrapping.

Bootstrapping recommender system and method

Embodiments are directed towards identifying auto-folder tags for messages by using a combinational optimization approach of bi-clustering folder names and features of messages based on relationship strengths. The combinational optimization approach of bi-clustering, generally, groups a plurality of folder names and a plurality of features into one or more metafolders to optimize a cost. The cost is based on an aggregate of cut relationship strengths, where a cut results when a relationship folder name and feature are grouped in separate metafolders. Furthermore, the plurality of folder names and the plurality of features are obtained by monitoring actions of a plurality of users, where the folder names are user generated folder names and features are from a plurality of messages. The metafolders may be used to tag new user messages with an auto-folder tag.

Mining global email folders for identifying auto-folder tags

Growing community of inventors

Zichron Yaakov, Israel

Yehuda Arie Koren