Amazon Technologies, Inc.

Grant Michael Emery

Vijai Mohan

Srikanth Thirumalai

Aswath Manoharan

Mark J Tomko

Egidio Terra

Madhu Madhava Kurup

Benjamin Thomas Cohen

Roshan Harish Makhijani

Scott Allen Mongrain

Logan Luyet Dillard

Rahul H Bhagat

Adam Callahan Sanders

Brian Cameros

Xiaoxin Yin

Ram Prasad Venkatesan

Michael David Quinn

Yongwen Liang

Gualtiero Forte

Eli Glen Rosofsky

Techniques for analyzing recommendations may be described. In particular, an item offered from a network-based resource may be selected based at least in part on past orders for the item. Recommended items offered from the network-based resource may also be identified. A determination may be made as to whether the recommended items may include the item. Based on this determination, an indication of an issue associated with recommending the item may be generated. Based on the indication, a workflow to identify a potential cause of the issue may be initiated.

Determining potential causes of an issue associated with recommendations and changing recommendation filter settings based on the outcome of an action

Disclosed are various embodiments for identifying correlations between child nodes of a taxonomy. A correlation service identifies a correlation between parent nodes of the taxonomy. A data set such as an interaction history is filtered according to the parent node correlation. The filtered data set is then used to identify correlations between the respective child nodes of the correlated parent nodes.

Identifying child node correlations

Features are disclosed for identifying randomized latent feature language modeling, such as a recurrent neural network language modeling (RNNLM). Sequences of item identifiers may be provided as the language for training the language model where the item identifiers are the words of the language. To avoid localization bias, the sequences may be randomized prior to or during the training process to provide more accurate prediction models.

Randomized latent feature learning

Features are disclosed for identifying and routing items for tagging using a latent feature model, such as a recurrent neural network language model (RNNLM). The model may be trained to identify latent features for catalog items such as movies, books, food items, beverages, and the like. Based on similarities in latent features, tags previous assigned to items may be applied to untagged items. Application may be manual or automatic. In either case, resources need to be balances to ensure efficient tagging of items. The included features help to identify and direct these limited tagging resources.

Latent feature based tag routing

The present technology may identify item category affinities by identifying a plurality of classifications of an item. An accuracy of the plurality of classifications relative to one another for the item may be identified. A category affinity of the item may be determined based on the accuracy of the plurality of classifications relative to one another.

Video classification

According to aspects of the disclosed subject matter, a method for identifying a set of documents from a document corpus that are potential duplicates of a source document, is provided. A source document is obtained. A list of queries corresponding to the source document is identified. Each query in the identified list of queries is executed on the document corpus, wherein the execution of each query yields a corresponding results set identifying an ordered set of documents in the document corpus. For each document identified in each results set, a document score is generated for the identified document based on the identified document's ordinal position in its results set. A subset of the identified documents of the results set is selected according to the generated document scores that satisfy predetermined selection criteria. The selected subset of identified documents are stored or displayed.

Identifying potential duplicates of a document in a document corpus

A computer system and method for determining whether the subject matter described in a received document is substantially similar to the subject matter of other documents in a document corpus, such that the received document can be considered a duplicate document. After receiving a first document, a set of tokens for the first document is generated. A non-fielded relevance search on a token index is executed. The relevance search returns a set of candidate duplicate documents with scores corresponding to each candidate document. For each candidate document with a score above a threshold, filtering is performed on each candidate document to determine whether each candidate document is a true duplicate of the first document. A set of candidate documents with a score above the threshold that were not disqualified as candidate documents is then provided.

Duplicate entry detection system and method

Systems and methods for determining a set of variation-phrases from a collection of documents in a document corpus is presented. Potential variation-phrase pairs among the various documents in the document corpus are identified. The identified potential variation-phrase pairs are then added to a variation-phrase set. The potential variation-phrase pairs in the variation-phrase set are filtered to remove those potential variation-phrase pairs that do not satisfy a predetermined criteria. After filtering the variation-phrase set, the resulting variation-phrase set is stored in a data store.

Determining variation sets among product descriptions

Systems and methods for filtering tokens from a document for determining whether the document describes substantially similar subject matter compared to another document are described. In one embodiment, a first document is obtained. This document is organized into a plurality of fields, and at least some of the fields include tokens representing the subject matter described by the document. A field of this document is selected and a token from within the selected field having the highest inverse document frequency (IDF) is selected. Those tokens that have a higher IDF than the selected token are removed. Using the remaining tokens, a determination is made as to whether the first document describes substantially similar subject matter to the subject matter described by a second document. An indication is provided as to whether the first document describes substantially similar subject matter to that described by a second document according to the determination.

Filtering invalid tokens from a document using high IDF token filtering

Systems and methods for determining whether a first document is a potential duplicate of a second document such that the two documents describe the same or substantially the same subject matter, wherein the first and second documents include attribute data in attribute fields. A set of rules is obtained for determining whether the first document is a potential duplicate of the second document. Moreover, for each rule in the set of rules, a determination is made as to whether data in a first set of attributes of the first document is contained in a second set of attributes of the second document. According to the results of the evaluated rules in the rules set, determining whether the first document is a potential duplicate of the second document. If, according to the evaluated rules in the rules set, the first document is determined to be a potential duplicate of the second document, storing a reference to the first document in a set of potential duplicates of the second document.

Comparison engine for identifying documents describing similar subject matter

According to aspects of the disclosed subject matter, a method for identifying a set of documents from a document corpus that are potential duplicates of a source document is provided. A source document is obtained. A list of queries corresponding to a source document is identified. Each query in the identified list of queries is executed on the document corpus, wherein the execution of each query yields a corresponding results set identifying an ordered set of documents in the document corpus. For each document identified in each results set, a document score is generated for the identified document based on the identified document's ordinal position in its results set. A subset of the identified documents of the results set is selected according to the generated document scores that satisfy predetermined selection criteria. The selected subset of identified documents are stored or displayed.

A system and method for determining the likelihood of two documents describing substantially similar subject matter is presented. A set of tokens for each of two documents is obtained, each set representing strings of characters found in the corresponding document. A matrix of token pairs is determined, each token pair comprising a token from each set of tokens. For each token pair in the matrix, a similarity score is determined. Those token pairs in the matrix with a similarity score above a threshold score are selected and added to a set of matched tokens. A similarity score for the two documents is determined according to the scores of the token pairs added to the set of matched tokens. The determined similarity score is provided as the likelihood that the first and second documents describing substantially similar subject matter.

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Seattle, WA, United States of America

Grant Michael Emery