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Enhancing Web Page Recommendations Through Sequence Mining in Access Log Analysis

This presentation explores the impact of sequence mining on web page recommendations in a recommender system driven by access logs. The system is designed for lightweight integration, allowing users to gain knowledge from frequently accessed web domains without deep technical involvement. We will discuss the architecture including data collection, recommendation generation, and the evaluation of page similarity through clustering. By analyzing user sessions and employing statistical methods, we demonstrate how sequence mining can significantly enhance the quality of recommendations in dynamic environments like MMORPGs.

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Enhancing Web Page Recommendations Through Sequence Mining in Access Log Analysis

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  1. by N. ThaiSon and L. Siemon Impact of Sequence Mining on Web-Page Recommendations in an Access-Log-Driven Recommender System

  2. Overviewof the Presentation 1 3 2 Evaluation Motivation The RS Project Report

  3. 1 Motivation and Domain Goals? Who can use the system?

  4. Content-Based RS in a Website Find an Implementation. Set up the Data Collection. And Display the Recommendations.

  5. Goals of our System AssumptionsUser frequently accesses a web-domain to acquire knowledge.Lightweight Light footprint on existing Source-Code. Easy-To-Implement The System should not require deep integration into the existing data.

  6. Powerful Graphic Software A typical user will only use a subset of the tools. While he learns the program he will visit the website. Example Website

  7. 2 System and Implementation Structure? What Technologies were used?

  8. Prototype Integration 1) Data Collector 2) Recommendation Generator 3) Recommendation Integration

  9. Sessions and Page Similarity The time-line of each user was split into Sessions. Then, by analyzing statistical co-occurrence of pages, we defined Page Similarity.

  10. Clustering of Pages Page Similarity=> Pages Clusters Pages clusters are core elements to analyze the similarity.

  11. Similarity between Users Basic Similarity Two users are similar if they accessed a similar subset of all clusters. Sequence Mining Considering the order of clusters a user accessed can help the quality of recommendations. User 1 User 2

  12. 3 Evaluation and Conclusion Did our prototype “work”? What else?

  13. MMORPGS as a Domain Diversity A player can take many decisions and there are different ways a character can progress. Similarity Many users will take similar decisions and hence require similar information. Searches What a user accesses in a database is highly related to his character progress.

  14. Data and Processing Filtering Filtering We had to filter our data to reduce the noise. Train and Test Data We split our data into train and test data by removing the last two clusters from each user. Split Data Generate Rec’s Evaluate

  15. Cluster Example

  16. Results and Evaluation

  17. Conclusion and Outlook What we did. We thought up a lightweight, easy-to-implement System and showed that Sequence Mining can improve recommendations. What else? The Parameters could be automatically generated. And we still don’t know when to recommend?

  18. Questions? What’s Your Message?

  19. Sources of Images http://www.tell.co.zw/wp-content/uploads/2011/11/difficult.jpghttp://www.mobileprofessionalsolutions.com/images/websites.jpg http://www.mint.com/blog/wp-content/uploads/2010/06/goal.jpg http://www.mricons.com/store/png/14624_14299_128_preferences_plugin_icon.png http://www.greekpublicpolicyforum.org/main/images/articles/forum.jpg http://blogs.sitepointstatic.com.s3.amazonaws.com/images/business/manual.jpg http://desmond.imageshack.us/Himg502/scaled.php?server=502&filename=fiestaonline8125403.jpg&res=landing http://www.saadkamal.com/wp-content/uploads/2008/10/search-engine-optimization-blog.jpg

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