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Reverse Engineering Static Content and Dynamic Behaviour of E-Commerce Sites for Fun and Profit

JP
February 03, 2016

Reverse Engineering Static Content and Dynamic Behaviour of E-Commerce Sites for Fun and Profit

2nd Presentation - Dissertation Planning
Faculdade de Engenharia da Universidade do Porto

JP

February 03, 2016
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  1. REVERSE ENGINEERING STATIC CONTENT AND DYNAMIC BEHAVIOUR OF E-COMMERCE SITES

    FOR FUN AND PROFIT João Pedro Dias Supervision: Hugo Sereno Ferreira, FEUP Rui Gonçalves, ShiftForward
  2. AGENDA 1. Context 2. Current Process 3. How Can We

    Improve It? 4. State of the Art 5. Proposed Methodology 2
  3. CONTEXT • “Electronic commerce is the process of buying, selling,

    transferring, or exchanging products, services, and/or information via computer networks, including the Internet.” In Overview of electronic commerce by Efraim Turban and David King (2011). 3
  4. CONTEXT • Increasing profit through the optimization of e-metrics like

    customer engagement and click-through rate. • Using target marketing activities and recommendations systems. • Mainly three approaches for building recommendation systems [CMS97]: 4 Collaborative filtering Recommend products to a user based on what similar users brought. Content-based filtering Recommend products that are similar to those that an user brought in the past. Hybrid recommender systems Recommendations are based on a combination of Collaborative filtering and Content-based filtering.
  5. CONTEXT 5 • Users typical interaction over an e-commerce website.

    • The user interaction data can be used influenced in order to increase profit.
  6. CONTEXT 6 • Data in e-commerce comes from different places.

    • Historical and real-time data need to be collected and then analyzed/transformed to be useful. Web Usage Web Structure Web Content
  7. New e- commerce website New historical and real- time data

    Machine Learning Practitioner • Almost every e-commerce website have particular characteristics: • Content presentation to the user; • Web site structure; • Web usage logs format. • For each website different and (partially) separated techniques are typically used to retrieve data from the various sources, making the process of data collecting more inefficient. • Some relationships between the sources can pass unnoticed due to the separated tasks. 7 CURRENT PROCESS
  8. 8

  9. HOW CAN WE IMPROVE IT? 9 A concise and all-

    in-one approach for data collection and knowledge retrieval Reduce the risk of not collecting some relevant data Less dedicated resources • Collection of information: • Web site content and structure (using crawlers). • Web site usage information (web server logs). • Statistical models, patterns and usage flows. • Taking advantage of used and proved methods.
  10. STATE OF THE ART | WEB MINING TAXONOMY • Web

    content mining can be defined as the scanning and mining of text, graphs and pictures from a Web page to find out the significance of the content to the search query. • Web structure mining analyses the organization of the content of the web where structure is defined by hyperlinks between pages and HTML formatting commands within a page. • Web usage mining is described as the application of data mining techniques on Web access logs to discover usage patterns and typical user’s flow. 10 [SA13] Web Mining Web Content Mining Web Usage Mining Web Structure Mining
  11. STATE OF THE ART | WEB MINING PROCESS 11 [LF10]

  12. STATE OF THE ART | • Web crawling • Common

    problems: URL extraction and canonicalization, spider traps. 12 DATA COLLECTION AND PREPROCESSING [PSM04]
  13. STATE OF THE ART | • Web Server Logs •

    Problems: incompletion, redundancy, ambiguity and noise (i.e. web bots). • Solutions: user and session identification and path completion. 13 DATA COLLECTION AND PREPROCESSING [LF10]
  14. STATE OF THE ART | • Application layer tracking (i.e.

    Google Analytics, Piwik) 14 DATA COLLECTION AND PREPROCESSING [Cut10]
  15. STATE OF THE ART | Exploratory Data Analysis Analyze data

    sets to summarize their main characteristics, often with visual methods. Clustering Grouping objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other clusters. Association rules Discovering interesting relations between variables in large datasets. Classification and prediction Classification models predict categorical class labels and prediction models predict continuous valued functions. Sequential and navigational pattern analysis Finding statistically relevant patterns between data objects where the values are delivered in a sequence or order. 15 PATTERN DISCOVERY AND ANALYSIS [Liu07]
  16. STATE OF THE ART | USER PROFILING • Keyword-based user

    profiles • Each keyword can represent a topic of interest. • Weights are attribute in order to represent the importance. • Common problem: Polysemy (words with similar meaning) • Semantic Networks • Weighted semantic network in which each node represents a concept. • Solves the polysemy problem of keyword-based profiles. Sports Keywords Ball Soccer … Weight 0,45 0,72 … Technology Keywords PC Mouse … Weight 0,64 0,33 … Music Keywords Rock Guitar … Weight 0,54 0,32 … 16 Example of a user profile based on keywords representation [GSCM07].
  17. STATE OF THE ART | USER PROFILING • Ontologies-based user

    profiles • Conceptualization of a domain into a human-understandable, but machine- readable format. • The user ontology captures metadata about the user’s profile including different characteristics (i.e. id entity, email, address, preferences) • Concept-based • Concepts are used instead of specific words. • Conceptual nodes and relationships are stablished. • Weighted concepts. 17 Excerpt of a concept-based user profile [GSCM07].
  18. PROPOSED METHODOLOGY 18 • An all-in-one method capable of extracting

    knowledge from: • Website content; • The links and relations between pages; • The historical and real-time usage data. • Resulting a data model representing an e-commerce website and its archetypical users. New e-commerce website New historical and real-time data All-in-one methodology for knowledge extraction Machine Learning Practitioner
  19. PROPOSED METHODOLOGY • Validation through the implementation of a proof

    of concept tool. • Using proper and representative data (real or similar to the data generated by an e-commerce web site). • Reducing the time collecting data about an e-commerce website using an concise and all-in-one process. • Get a faster overview about the user typical behavior without resorting to real-time application tracking (application layer) 19
  20. WORK PLAN • 12/02/2016 (Done) • Art’s state analysis (typical

    approaches and problems/solutions). • Development of a crawler prototype and lookup of several server log samples. • 12/02/2016 – 20/03/2016 (1 months) • Exploratory development of key features and techniques. • Crawling and other data collection techniques, pattern discovery techniques, user profile representations. • 18/03/2016 – 24/04/2016 (1.5 months) • Implement selected features and techniques as a proof of concept tool. • 11/04/2016 – 23/05/2016 (2 months) • Test and improvement. • Experimental evaluation. • 23/05/2016 – 24/06/2016 (1 month) • Dissertation write up. • 24/06/2016 – 25/07/2016 (1 month) • Dissertation delivery and presentation. 20
  21. Thank you! New e- commerce website New historical and real-

    time data Machine Learning Practitioner New e- commerce website New historical and real-time data All-in-one methodology for knowledge extraction Machine Learning Practitioner Improve
  22. REFERENCES • [PMS04] Gautam Pant, Padmini Srinivasan, and Filippo Menczer.

    Crawling the Web. Web Dynamics, pages 153-177, 2004. • [GSCM07] Susan Gauch, Mirco Speretta, Aravind Chandramouli, and Alessandro Micarelli. User Profiles for Personalized Information Access. The Adaptive Web, 4321:54-89, 2007. • [Liu07] Bing Liu. Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media, 2007. • [Cut10] Justin Cutroni. Google Analytics, volume 1. O’Reilly Media, Inc., First edition, 2010. • [CMS97] R Cooley, B Mobasher, and J Srivastava. Web mining: information and pattern discovery on the World Wide Web. In IEEE International Conference on Tools with Artificial Intelligence, pages 558–567, 1997. • [LF10] Li Mei and Feng Cheng. Overview of Web mining technology and its application in e-commerce. 2010 2nd International Conference on Computer Engineering and Technology, 7:277–280, 2010. • [SA13] Ahmad Siddiqui and Sultan Aljahdali. Web Mining Techniques in E-Commerce Applications. International Journal of Computer Applications, 69(8):39–43, may 2013. 22