Ebay, Incorporated

Amazon Technologies, Inc.

Chandra Prakash Khatri

Raefer Christopher Gabriel

Anushree Venkatesh

Behnam Hedayatnia

Praveen K Boinapalli

Steven Hui Luan

Michael Tanaka

Ariya Rastrow

Arindam Mandal

Selcuk Kopru

Ankur Gandhe

Tagyoung Chung

Rahul Goel

Nish Parikh

Justin Nicholas House

Sameep Navin Solanki

Anirudh Raju

Sanghyun Yi

Ahmad Shikib Mehri

One or more of the systems, apparatuses, or methods discussed herein can include a quality score for a plurality of item listings or collections of item listings. Data sparseness can be avoided, as the quality score is based on inherent properties of the listing. An item listing can be recommended to a user based on the quality score. In one or more embodiments, a method can include determining a plurality of quality scores including a quality score for each of a plurality of item listings or a plurality of collections of item listings, the quality scores determined independent of a user's attributes and independent of the user's contextual information, the contextual information corresponding to details of the user's access to a website, and recommending an item listing or collection of item listings to a user based on the quality scores and the contextual information.

Systems, apparatuses, and methods for providing a quality score based recommendation

Systems, apparatuses, and methods for providing a ranking based recommendation

A system that can engage in a dialog with a user may select a system response to a user input based on how the system estimates a user may respond to a potential system response. Models may be trained to evaluate a potential system response in view of various available data including dialog history, entity data, etc. Each model may score the potential system response for various qualitative aspects such as whether the response is likely to be comprehensible, on-topic, interesting, likely to lead to the dialog continuing, etc. Such scores may be combined to other scores such as whether the potential response is coherent or engaging. The models may be trained using previous dialog/chatbot evaluation data. At runtime the scores may be used to select a system response to a user input as part of the dialog.

Dialog response generation

Described herein is a system for rescoring automatic speech recognition hypotheses for conversational devices that have multi-turn dialogs with a user. The system leverages dialog context by incorporating data related to past user utterances and data related to the system generated response corresponding to the past user utterance. Incorporation of this data improves recognition of a particular user utterance within the dialog.

Speech recognition using dialog history

In various example embodiments, a system and method for a Target Language Engine are presented. The Target Language Engine augments a synonym list in a base dictionary of a target language with one or more historical search queries previously submitted to search one or more listings in listing data. The Target Language Engine identifies a compound word and a plurality of words present in the listing data that have a common meaning in the target language. Each word from the plurality of words is present in the compound word. The Target Language Engine causes a database to create an associative link between the portion of text and a word selected from at least one of the synonym list or the plurality of words.

Snippet generation and item description summarizer

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San Jose, CA, United States of America

Chandra Prakash Khatri