Data Mining for Web Personalization
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1 Data Mining for Web Personalization Patrick Dudas Outline Personalization Data mining Examples Web mining MapReduce Data Preprocessing Knowledge Discovery Evaluation Information High 1
2 Personalization Goal of data mining approach is for automatic personalization Automatic Personalization: Content-based Collaborative Rule-based Rule-based (Brief overview) Create decision rules Implicitly/Explicitly Highly domain dependent Rules nontransferable Profiles are based on user input Biased Static Degrade over time 2
3 Content-based (Brief overview) Profile based on users past experiences and their interest (ratings) Think Amazon, Pandora, ebay.. Vector similarities based on cosine similarity Bayesian classification Remember: Ratings = Profile = Recommendation Collaborative (Brief overview) Creating groups of users based on ratings Nearest neighbor approach Once grouped, recommendation based on the other neighbors are presented More users or items = more dimensions of data Dynamic or real-time not applicable 3
4 Data Mining Data rich descriptions Large volumes of data reliable models Automated data collection Evaluate results/make decisions Integration with existing data sources Examples of Large Datasets Featured data sets: Illumina - Jay Flatley (CEO of Illumina) Human Genome Data Setcience 315(5814): GB YRI Trio Dataset 700GB Sloan Digital Sky Survey DR6 Subset 160 GB Genome, survey data, Google Books n-gram corpuses, traffic statistics, OpenStreetMap dataset, Wikipedia traffic 4
5 Data Mining Web Personalization Recommendations based on Web objects: Items Pages Documents Navigation by links Web mining Pros: Personalization (duh.), real-time, more enriched datasets Cons: Privacy issues, building complex systems that misrepresent the individual Extend the Data Mining Paradigm 3) Recommendation 1) Data Preparation and Transformation 2) Pattern Discovery 5
6 Data Preparation and Transformation Web logs Date/time usage Site information Resource requested (image, video, etc.) Site files/meta-data The power of the cookie Server-side cookies! Data Preparation and Transformation (cont.) Pageview: User actions (where they clicked and the path) User events (what they are trying to accomplish) Session: Sequence of page views 6
7 MapReduce Google design Hoodop implemented C++, C#, Erlang, Java, Ocaml, Perl, Python, Ruby, F#, R.. Example 7
8 Usage Data Pre-Processing Pattern Discovery We have data! Now what? Cluster Classification Association Rule Discovery Sequential pattern Discovery Markov Models Latent Variable Model 8
9 Clustering Partitioning Split your data into groups K-means Hierarchical Divisive (top-down) Start with everything, find groups Agglomerative (bottom-up) Start with a cluster and add additional information Model-based Building a model for the data (best fit) K-means 9
10 User-Based Clustering Start with the user profile Partition into k-groups of profiles Based on similarity Association Discovery Support min(support) Confidence min(confidence) 10
11 Evaluation (Personalization Model) Challenges: Recommendation algorithms may require unique set of evaluation metrics Personalization actions may be different Domain Intended application Data gathered Check for overfitting data Training set ROC Curve ROC Curve TPR = TP / (TP + FN) FPR = FP / (FP + TN) "Yes" "No" SN N
12 Information High Information is addictive Information can be misleading Information ethics Information is power, sometimes too powerful Personal Suggestions Develop a hypothesis Figure out what data is needed Make informed decisions Don t trust just your judgment Experts are experts for a reason! Develop a way to validate based on experience Then get more data if needed 12
13 Sources Kohavi, R. and F. Provost (2001). "Applications of data mining to electronic commerce." Data Mining and Knowledge Discovery 5(1): Dean, J. and S. Ghemawat (2008). "MapReduce: Simplified data processing on large clusters." Communications of the ACM 51(1): Mobasher, B. (2007). "Data mining for web personalization." The adaptive web: Zaharia, M., A. Konwinski, et al. (2008). Improving mapreduce performance in heterogeneous environments, USENIX Association. Witten, I. H. and E. Frank (2002). "Data mining: practical machine learning tools and techniques with Java implementations." ACM SIGMOD Record 31 (1): Senthil kumar, A. (2011). Knowledge discovery practices and emerging applications of data mining : trends and new domains. Hershey, PA, Information Science Reference. Thank you! Questions? 13
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