1. Web Usage Mining Web Usage Mining (Clickstream Analysis) Mark Levene (Follow the links to learn more!)

2. Reminder - W3C Extended Log File FormatW3C Extended Log File Format cs = client-to-server actions s = server actions c = client actions sc = server-to-client actions

3. Analog Analog – Web Log File Analyser Gives basic statistics such as –number of hits –average hits per time period –what are the popular pages in your site –who is visiting your site –what keywords are users searching for to get to you –what is being downloaded Log data does not disclose the visitors identity What do analogs reports mean?mean Report for www.dcs.bbk.ac.uk/~markReport

4. Applications of Usage Mining Pre-fetching and caching web pages eCommerce and clickstream analysis Web site reorganisation Personalisation Recommendation of links and products

5. Identification of User By IP address –Not so reliable as IP can be dynamic –Different users may use same IP Through cookies –Reliable but user may remove cookies –Security and privacy issues Through login –Users have to register

6. Sessionising Time oriented (robust) –By total duration of session not more than 30 minutes –By page stay times (good for short sessions) not more than 10 minutes per page Navigation oriented (good for short sessions and when timestamps unreliable) –Referrer is previous page in session, or –Referrer is undefined but request within 10 secs, or –Link from previous to current page in web site

7. Mining Navigation Patterns Each session induces a user trail through the site A trail is a sequence of web pages followed by a user during a session, ordered by time of access. A pattern in this context is a frequent trail. Co-occurrence of web pages is important, e.g. shopping-basket and checkout. Use a Markov chain model.

8. Trails inferred from Log data (Each session results in a trail) IDTrail 1A1 > A2 > A3 2 3A1 > A2 > A3 > A4 4A5 > A2 > A4 5A5 > A2 > A4 > A6 6A5 > A2 > A3 > A6

9. Construct Markov Chain from Data Add a unique start state. –the start state has a transition to all visited web pages in the site. Add a unique final state. –the last page in each trail has a transition to the final state. The transition probabilities are obtained from counting click-throughs. The Markov chain built is called absorbing since we always end up in the final state.

10. The Markov Chain from the Data

11. Support and Confidence Support s in [0,1) – accept only trails whose initial probability is above s. –Setting support to be above the average click- through is reasonable. Confidence c in [0,1) – accept only trails whose probability is above c. –The probability of a trail is obtained by multiplying the transition probabilities of the links in the trail.

12. Mining Frequent Trails Find all trails whose initial probability is higher than s, and whose trail probability is above c. Use depth-first search on the Markov chain to compute the trails. The average time needed to find the frequent trails is proportional to the number of web pages in the site.

13. Frequent Trails Support = 0.1 and Confidence = 0.3 TrailProbability A1 > A2 > A30.67 A5 > A2 > A30.67 A2 > A30.67 A1 > A2 > A40.33 A5 > A2 > A40.33 A2 > A40.33 A4 > A60.33

14. Frequent Trails Support = 0.1 and Confidence = 0.5 TrailProbability A1 > A2 > A30.67 A5 > A2 > A30.67 A2 > A30.67

15. Content Mining Incorporate the categories that users are navigating through so we may better understand their activities. –E.g. what type of book is the user interested in; this may be used for recommendation. Classify users according to behaviour. –Is the users intent to browse, search or buy? Cluster users with common interests.

16. Pre-fetching and Caching Pages Learn access patterns to predict future accesses. Pre-fetch predicted pages to reduce latency. Can use Markov model and base the prediction on history of access. Also cache results of popular search engine queries.

17. ECommerce Click stream Analysis What is the users intention: browse, search or buy? Measure time spent on site - site stickiness Repeat visits – it has been shown that repeat visitors spend less time on the site; can be explained by learning. Measure visit-to-purchase conversion ratio, and predict purchase likelihood.

18. Supplementary Analyses to Improve eCommerce Web Sites Detecting visits from crawlers as opposed to human visitors. Form error analysis, e.g. login errors, mandatory fields not filled, incorrect format. When and why do people exit the site, e.g. visitor puts item in cart but exists before reaching the checkout. Analysis of local search engine logs – correlate with site behaviour. Product recommendations based on association rules (people who bought x also bought y). Geographic analysis – where are the customers? Demographic analysis – who are the customers?

19. Adaptive web sites Modify the web site according to user access. –Automatic synthesis of index pages (hubs that contain links on a specific topic) –Based on a clustering algorithm that uses the co-occurrence frequencies of pages from the log data. –Finds a concept that best describes each cluster.