1. Dr. Guandong Xu Intelligent Web & Information Systems (IWIS) Department of Computer Science, Aalborg University Web Usage Mining & Personalization

2. References MOBASHAR, B., Chap4: Web Usage Mining and Personalization. In Practical Handbook of Internet Computing, Munindar P. Singh (ed.), CRC Press. 2005 XU, G., ZHANG, Y., & LI, L., Web Mining and Social Networking : Techniques and Applications, Springer, Nov, 2010, http://www.springer.com/978-1-4419- 7734-2http://www.springer.com/978-1-4419- 7734-2

3. Personalization Web personalization can be described as any action that makes the Web experience of a user customized to the user’s taste or preferences. Principal elements of Web personalization include modeling of Web objects (such as pages or products) and subjects (such as users or customers), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization

4. Personalization categories three general groups: manual decision rule systems - rules based on user demographics or static profiles (collected through a registration process); content-based filtering agents - rely on personal profiles and the content similarity of Web documents collaborative filtering systems- user ratings or preferences, and through a correlation engine.

5. Drawbacks of current systems Rule-based filtering Subjective description of users Prone to biased Static user profile – degrade the performance after time Content-based filtering Rely on content similarity, not feasible for non-textual resource Missing the consideration of user preference Collaborative filtering – a predominant approach in most commercial e-commerce systems Instead of the content features, it involves in matching the ratings of like-minded users for objects (e.g. movies or products) Most commonly used KNN algorithm

6. Limitations of collaborative filtering The high dimensionality of items in the system The sparsity of rating data – decrease the likelihood of a significant overlap of ratings The high computational cost of real-time prediction the necessity of integrating the explicit user ratings and implicit content or product-oriented features

7. Improvement on these concerns Optimization strategies, such as Similarity indexing and dimensionality reduction Offline clustering of user records – search only within a matching cluster Integration of content and user demographics A promising technique – web usage mining Goal is to capture and model the patterns and profiles of users interacting with a Web site

8. The aims of web usage mining The discovered patterns are usually represented as collections of pages or items that are frequently accessed by groups of users with common needs or interests. Such patterns can be used to better understand behavioral characteristics of visitors or user segments, improve the organization and structure of the site, and create a personalized experience for visitors by providing dynamic recommendations.

9. Aspects of web usage mining Enhance the above discussed approaches and remedy the shortcomings Benefits from the advance of data mining – employing data mining algorithms on the offline pattern discovery from user transaction – improve the scalability of CF Clustering Association rule mining Navigation pattern mining and so on

10. Personalization based on WUM Goal: to recommend a set of objects, e.g. links, ads, text, products or service tailored to the user’s preference Personalization process Derive the navigational patterns of users – “long-term” (the user activity history) or “short-term” (single sessions) – web log mining or analysis Match the target active user with the derived patterns Make recommendations via the matched pattern (recommendation engine)

11. The overview of web personalization based on WUM Consists of three phases data preparation and transformation, pattern discovery, and recommendation ( real-time)

12. The offline data preparation and pattern discovery components

13. The online personalization component

14. Data Preparation and Modeling the most time consuming and computationally intensive step in the knowledge discovery process requires the use of especial algorithms and heuristics not commonly employed in other domains critical to the successful extraction of useful patterns from the data

15. Sources and Types of Data Usage data: log file Content data: textual Structure data: linkage map User data: demographic, domain know…

16. Usage data preparation data preprocessing include data cleaning, pageview identification, user identification, session identification (or sessionization), the inference of missing references due to caching, and transaction (episode) identification SESSION #925 (USER_ID = 338) 9745438/news/default.asp- 9745452/admissions/ >/news/default.asp 9745520/admissions/requirements.asp > /admissions/ 9745846/programs/ >/admissions/requirements.asp 9745852/programs/2002/gradect2002.asp > /programs/ 9745907/pdf/promos/2002/ect2002.pdf > /programs/2002/gradect2002.asp PageurlDuration(s)weight(%) /news/default.asp:142.98 /admissions/6814.5 /admissions/requirements.asp32669.51 /programs/61.28 /programs/2002/gradect2002.asp5511.73 /pdf/promos/2002/ect2002.pdf……

17. Web usage data model The data preprocessing results in User sessions, S={S i |i=1,…,m} Pages corpus, P={P j |j=1,…,n} Each user session s i ={,…, }, or simply s i ={w i1, w i2 …,w in }

18. Data integration from multiple source

19. Data integration example TP matrix: transaction-pageview ∈ R m×n PF matrix: pageview-feature ∈ R n×k Multiply TP with PF : TP ×PF=TP={t 1 ’,t 2 ’,…,t m ’} ∈ R m×k t j ’ is a k-dimensional vector over the feature space a user transaction can be represented as a content feature vector, reflecting that user’s interests in particular concepts or topics

20. Pattern discovery from usage data Employ various DM & ML approaches Standard statistical techniques for user behavior On more integrated data warehouse

21. Data mining approaches for usage data Given a set of n pageviews, P = {p 1, p 2, · · ·, p n }, and a set of m user transactions, T = {t 1, t 2, · · ·, t m }, where each t i ∈ T is a subset of P. Each transaction t as an l-length sequence of ordered pairs: t =, where each p t i = p j for some j ∈ {1, · · ·, n}, and w(p t i ) is the weight associated with pageview p t i in the transaction t.

22. Association rule mining Given a transaction T and a set I = {I 1, I 2,..., I k } of frequent itemsets over T. The support of an itemset I i ∈ I is defined as An association rule r is an expression of the form X ⇒ Y ( σ r, α r ), where X and Y are itemsets, σ r = σ (X ∪ Y ) is the support of X ∪ Y representing the probability that X and Y occur together in a transaction. The confidence for the rule r, α r, is given by σ (X ∪ Y ) / σ (X) and represents the conditional probability that Y occurs in a transaction given that X has occurred in that transaction.

23. An association rule example For example, a high-confidence rule such as {special-offers/, /products/software/} ⇒ {shopping-cart/} might provide some indication that a promotional campaign on software products is positively affecting online sales. Such rules can also be used to optimize the structure of the site. For example, if a site does not provide direct linkage between two pages A and B, the discovery of a rule {A} ⇒ {B} would indicate that providing a direct hyperlink might aid users in finding the intended information.

24. Sequential pattern mining Given a transaction set T and a set S = {S 1, S 2,..., S n } of frequent sequential (respectively, contiguous sequential) pattern over T, the support of each S i is defined as follows: The confidence of the rule X ⇒ Y, where X and Y are (contiguous) sequential patterns, is defined as where ◦ denotes the concatenation operator

25. Clustering approaches A process to assign data objects into various data groups or categories based on the similarity or distance between the objects such that the intra-group similarity within one group is maximized but the inter-group similarity is minimized. A unsupervised approach only relying on the mutual similarity, in contrast, classification is supervised learning In the context of usage data, two types of clustering : clustering the transactions (or users), or clustering pageviews Applications of clustering in Web usage mining, e-marketing, personalization, and collaborative filtering

26. Clustering in user profiling An example of deriving aggregate usage profiles from transaction clusters

27. Steps of clustering in user profiling Given the mapping of user transactions into a multi-dimensional space as vectors of pageviews (i.e., the matrix TP)

28. Steps of clustering in user profiling Given the mapping of user transactions into a multi-dimensional space as vectors of pageviews (i.e., the matrix TP) Employ standard clustering algorithms, such as k-means, generally partition this space into subgroups Obtain user segments, but not capturing an aggregated view of common user patterns

29. Steps of clustering in user profiling Given the mapping of user transactions into a multi-dimensional space as vectors of pageviews (i.e., the matrix TP) Employ standard clustering algorithms, such as k-means, generally partition this space into subgroups Obtain user segments, but not capturing an aggregated view of common user patterns Utilize the centroid (or the mean vector) of each cluster to represent the aggregated view of user pattern

30. Discovered Patterns for Personalization several effective recommendation algorithms based on clustering (which can be seen as an extension of standard kNN- based collaborative filtering), association rule mining (AR), and sequential pattern (SP) or contiguous sequential pattern (CSP) discovery.

31. kNN-Based Approach k-Nearest-Neighbor (kNN) approach involves comparing the activity record for a target user with the historical records of other users in order to find the top k users who have similar tastes or interests. Measuring the similarity or correlation between the active session s and each transaction vector t (where t ∈ T ). The top k most similar transactions to s are considered to be the neighborhood for the session s, which denoted by NB(s) (taking the size k of the neighborhood to be implicit) NB(s) = {t s 1,t s 2, · · ·,t s k }. Various similarity measure – Pearson correlation coefficient; cosine coefficient; Jaccard distance and so on.

32. Recommendation score Given the active use session s and the nearest neighbors NB(s), the recommendation score of an item is where weight(p,NB(s)) is the mean weight for pageview p in the neighborhood as expressed in the centroid vector

33. Using Clustering For Personalization A method PACT (Profile Aggregation Based on Clustering Transactions) [Mobasher et al., 2002] an aggregate usage profile pr c as a set of pageview-weight pairs: pr c = { | p ∈ P, weight(p, pr c ) ≥ μ }, where the significance weight, weight(p, pr c ), of the pageview p within the usage profile pr c Recommendation score

34. Association Rules for Personalization Sample Web Transactions involving pageviews A, B, C, D and E Example of discovered frequent itemsets

35. An example of a Frequent Itemsets Graph (frequency threshold of 4) Now, given user active session window B,E, the recommendation generation algorithm finds items A and C as candidate recommendations. The recommendation scores of item A and C are 1 and 4/5, corresponding to the confidences of the rules {B,E} → {A} and {B,E} → {C}, respectively.

36. Sequential Patterns for Personalization Example of discovered sequential patterns

37. Example of discovered contiguous sequential patterns

38. Recommendation example Give a user’s active session window A,B, the recommendation engine using sequential patterns finds item E as a candidate recommendation. The recommendation score of item E is 1, corresponding to the rule A,B ⇒ E. On the other hand, the recommendation engine using contiguous sequential patterns will, in this case, fails to give any recommendations

39. Thanks!