Detecting Online Commercial Intention (OCI) Honghua Dai, Zaiqing Nie, Lee Wang, Lingzhi Zhao, Ji-Rong Wen, Ying Li WWW’ 06 Advisor: Chia-Hui Chang Student: Teng-Kai Fan Date: 2009-08-24

Outline Introduction Defining (OCI): Online Commercial Intention Learning Online Commercial Intention Web Page OCI Detector Query OCI Detector Experiment Conclusion

Introduction

Defining (OCI): Online Commercial Intention

Learning Online Commercial Intention

Web Page OCI Detector

Query OCI Detector

Experiment

Conclusion

Introduction Two major online user activities: Browsing activity Searching activity Three categories for user’s search intention: Navigational: reach to a particular web site. Informational: acquire information on web pages. Transactional: perform some “web-mediated” activity.

Two major online user activities:

Browsing activity

Searching activity

Three categories for user’s search intention:

Navigational: reach to a particular web site.

Informational: acquire information on web pages.

Transactional: perform some “web-mediated” activity.

Introduction cont. OCI (Online Commercial Intention) : understanding whether a user has intention to purchase or participate in commercial service.

OCI (Online Commercial Intention) : understanding whether a user has intention to purchase or participate in commercial service.

Defining OCI (Online Commercial Intention) Defining OCI to be a function from a query or a Web page to a binary value: Commercial or Non-Commercial. The goal is to compute two functions OCI : Q -> {Commercial, Non-Commercial} OCI : P -> {Commercial, Non-Commercial}

Defining OCI to be a function from a query or a Web page to a binary value: Commercial or Non-Commercial.

The goal is to compute two functions

OCI : Q -> {Commercial, Non-Commercial}

OCI : P -> {Commercial, Non-Commercial}

Learning Online Commercial Intention Taxonomy-based Using existing concept hierarchies or categories. Machine learning approach Extracting features from page content and building the classifiers based on those features. Labeling Process: Human-evaluation approach.

Taxonomy-based

Using existing concept hierarchies or categories.

Machine learning approach

Extracting features from page content and building the classifiers based on those features.

Labeling Process: Human-evaluation approach.

Web Page OCI Detector Input: a Web Page P Output: OCI (commercial or non-commercial) of P SVM

Input: a Web Page P

Output: OCI (commercial or non-commercial) of P

Keyword Extraction and Selection Keyword extraction: both inner text and tag attributes of all the training data. Feature selection: Pr( k | C ): the probability of the keyword k occurring in a Web page belonging to class C.

Keyword extraction: both inner text and tag attributes of all the training data.

Feature selection:

Pr( k | C ): the probability of the keyword k occurring in a Web page belonging to class C.

Keyword Extraction and Selection cont. Define two aspects of properties for each keyword k in a page p : For a page p with n keywords can be represented in 2*n dimensions:

Define two aspects of properties for each keyword k in a page p :

For a page p with n keywords can be represented in 2*n dimensions:

Query OCI Detector Four type of data sources for query OCI: Constituent terms of search query. Ex.: “airline ticket deals ”, “digital camera price ”. Content of top landing pages recommended by search engine. Content of search result page. Including title, short descriptions, and URL links. The number of user clicks of landing pages recommended by search engine.

Four type of data sources for query OCI:

Constituent terms of search query.

Ex.: “airline ticket deals ”, “digital camera price ”.

Content of top landing pages recommended by search engine.

Content of search result page.

Including title, short descriptions, and URL links.

The number of user clicks of landing pages recommended by search engine.

Detecting OCI based on Top Search Result Landing Pages Using top-10 result pages generated by MSN. Using the Web page OCI detector to detect the OCI of top 10 landing pages.

Using top-10 result pages generated by MSN.

Using the Web page OCI detector to detect the OCI of top 10 landing pages.

Detecting OCI based on Top Search Result Landing Pages cont.

Detecting OCI based on First Search Result Page

Experiments Data 1408 US English queries. Collect the first search result page for 1408 queries. Collect the top 10 landing pages for 1408 queries Randomly pick 26186 English Web pages. Labeling Analysis

Data

1408 US English queries.

Collect the first search result page for 1408 queries.

Collect the top 10 landing pages for 1408 queries

Randomly pick 26186 English Web pages.

Labeling Analysis

Evaluation Methodology For Web OCI detector, due to unbalanced problem , they selected all commercial pages and the equals number of non-commercial to train model. For query OCI detector: Compare the model based on first search result page and top N result landing pages . Using 3-fold cross validation. Measures: Precision, Recall and F-Measure

For Web OCI detector, due to unbalanced problem , they selected all commercial pages and the equals number of non-commercial to train model.

For query OCI detector:

Compare the model based on first search result page and top N result landing pages .

Using 3-fold cross validation.

Measures: Precision, Recall and F-Measure

Evaluating Page OCI Detector CP (Precision), CR (Recall), CF (F-measure)

CP (Precision), CR (Recall), CF (F-measure)

Evaluating Page OCI Detector cont.

Evaluating Query OCI Detector

OCI Analysis for a Stratified Query Sample based on Query Frequency Divided query frequency into 5: Single, Very low, Low, Mid, and High. Randomly select 10000 queries for each level. Observation: Query set with high frequency have larger portion of queries with commercial intention.

Divided query frequency into 5: Single, Very low, Low, Mid, and High.

Randomly select 10000 queries for each level.

Observation: Query set with high frequency have larger portion of queries with commercial intention.

Conclusion They present the framework of building machine learning models to learn OCI (queries and Web pages) based on any web page content.

They present the framework of building machine learning models to learn OCI (queries and Web pages) based on any web page content.