Miscategorized outlier detection using unsupervised slm-gbm approach and structured data

Patent metadata

Field	Value
Publication number	US-2017083602-A1
Application number	US-201514861746-A
Country	US
Kind code	A1
Filing date	Sep 22, 2015
Priority date	Sep 22, 2015
Publication date	Mar 23, 2017
Grant date	—

Abstract

In an example, one or more leaf category specific unsupervised statistical language model (SLM) models are trained using sample item listings corresponding to each of one or more leaf categories and structured data about the one or more leaf categories, the training including calculating an expected perplexity and a standard deviation for item listing titles. A perplexity for a title of a particular item listing is calculated and a perplexity deviation signal is generated based on a difference between the perplexity for the title of the particular item listing and the expected perplexity for item listing titles in a leaf category of the particular item listing and based on the standard deviation for item listing titles in the leaf category of the particular item listing. A gradient boosting machine (GBM) fuses the perplexity deviation signal with one or more other signals to generate a miscategorization classification score corresponding to the particular item listing.

First claim

What is claimed is:

1 . A system comprising:
a statistical language model (SLM) training component executable by one or more processors and configured to train one or more leaf-category-specific unsupervised statistical language model (SLM) models using sample item listings corresponding to each of one or more leaf categories and structured data about the one or more leaf categories, the training including calculating an expected perplexity and a standard deviation for item listing titles; a perplexity deviation signal generator configured to, in response to a request for a miscategorization classification score corresponding to a particular item listing:
calculate a perplexity for a title of the particular item listing, and
generate a perplexity deviation signal based on a difference between the perplexity for the title of the particular item listing and the expected perplexity for item listing titles in a leaf category of the particular item listing and based on the standard deviation for item listing titles in the leaf category of the particular item listing; and
a gradient boosting machine (GBM) configured to fuse the perplexity deviation signal with one or more other signals to generate a miscategorization classification score corresponding to the particular item listing.

2 . The system of claim 1 , wherein the training further includes generating an SLM for each leaf category for structured data, an SLM for each leaf category's queries, and an SLM for each leaf category's titles, and interpolating the SLM for each leaf category for structured data, the SLM for each leaf category's queries, and the SLM for each leaf category's titles into an SLM for each leaf category.

3 . The system of claim 2 , wherein the training further includes generating an expected perplexity and a standard deviation for each leaf category based on the SLM for each leaf category and perplexity and standard deviation calculations for each sample item listing.

4 . The system of claim 1 , wherein the generating the perplexity deviation signal includes computing a sentence log probability.

5 . The system of claim 1 , further comprising:
a GBM training component configured to:
create a tuning set of item listings by labeling item listings as miscategorized or non-miscategorized based on application of filters to the item listings; and
feed the tuning set of item listings to the GBM for tuning of a GBM model used by the GBM.

6 . The system of claim 1 , wherein the GBM takes a product type signal as input.

7 . A method comprising:
training one or more leaf-category-specific unsupervised statistical language model (SLM) models using sample item listings corresponding to each of one or more leaf categories and structured data about the one or more leaf categories, the training including calculating an expected perplexity and a standard deviation for item listing titles; in response to a request for a miscategorization classification score corresponding to a particular item listing, calculating a perplexity for a title of the particular item listing and generating a perplexity deviation signal based on a difference between the perplexity for the title of the particular item listing and the expected perplexity for item listing titles in a leaf category of the particular item listing and based on the standard deviation for item listing titles in the leaf category of the particular item listing; and using a gradient boosting machine (GBM) to fuse the perplexity deviation signal with one or more other signals to generate a miscategorization classification score corresponding to the particular item listing.

8 . The method of claim 7 , wherein the training comprises calculating a sentence perplexity PP(S) for each sequence S of N words {w 1 , w 2 , . . . , w N } in each title of each of the sample item listings according to the following formula:

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9 . The method of claim 7 , wherein the training further includes generating an SLM for each leaf category for structured data, an SLM for each leaf category's queries, and an SLM for each leaf category's titles, and interpolating the SLM for each leaf category for structured data, the SLM for each leaf category's queries, and the SLM for each leaf category's titles into an SLM for each leaf category.

10 . The method of claim 9 , wherein the training further includes generating an expected perplexity and a standard deviation for each leaf category based on the SLM for each leaf category and perplexity and standard deviation calculations for each sample item listing.

11 . The method of claim 7 , wherein the generating the perplexity deviation signal includes computing a sentence log probability.

Assignees

• Ebay Inc

Inventors

• Liu Mingkuan

Classifications

• G06F16/215 Primary

  Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors · CPC title

• G06F40/284

  Lexical analysis, e.g. tokenisation or collocates · CPC title

• G06F40/211

  Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars · CPC title

• G06F40/216

  using statistical methods · CPC title

• G06F16/285 Primary

  Clustering or classification · CPC title