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Method of Accurately Predicting a ClickThrough Rate (CTR) Using a Topic Based Co-Relevance Model (TCRM)

IP.com Disclosure Number: IPCOM000200638D
Publication Date: 2010-Oct-22
Document File: 3 page(s) / 38K

Publishing Venue

The IP.com Prior Art Database

Related People

Yu Zou: INVENTOR [+5]

Abstract

A method of accurately predicting a ClickThrough Rate (CTR) using a topic based co-relevance model (TCRM) is disclosed.

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Method of Accurately Predicting a ClickThrough Rate (CTR) Using a Topic Based Co-Relevance Model (TCRM)

Abstract

A method of accurately predicting a ClickThrough Rate (CTR) using a topic based co-relevance model (TCRM) is disclosed.

Description

Disclosed is a method of accurately predicting a ClickThrough Rate (CTR) using a topic based co-relevance model (TCRM).  The TCRM includes a latent topic model to map documents including both pages and advertisements into a latent semantic space and a co-relevance model to measure the co-relevancy of a topic in a webpage and a topic associated with advertisement candidates.

The latent topic model models documents with a finite mixture model of k topics and estimates the model parameters by fitting the data with the finite mixture model.  Here, a Probabilistic Latent Semantic Analysis (PLSA) model is used to deduce probability distribution of topics for documents including both web pages and advertisement.  The PLSA model projects a webpage and the corresponding advertisement into the same topic space, wherein each webpage and advertisement is a vector in the probability topic space.  Alternatively, the topic space of the webpage and the topic space of the advertisements may be trained separately.

The co-relevance model is a matrix of CTRs of all (page_topic, ad_topic) pairs, which describes the probability of a user clicking on one or more advertisements corresponding to a topic after the user has read pages about another topic.  Additionally, the topic co-relevance model may be trained using machine learning methods from click or impression data.

To predict a CTR, the CTR of a given (page, ad) pair is re-composed in the cross topic space by the following formulae:

or

if written in matrix form, where

In this formula, the corresponding CTR on topic level is used as CTR of <page, ad> pair, because page and ad are children of topics in the concept hierarchy and children inherit statistical data of a parent according to s...