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NATO Advanced Study Institute on Mining Massive Data Sets for Security

User logs processing using machine learning techniques

author: Thierry Artieres, Paris VI University

Description

User modeling is progressively becoming an important and generic component of many applications and services. The mains reasons that explain this phenomenon are the tasks increasing complexity and the wide variety of users.
nformation systems, hypermedia, websites, and application software are becoming more and more complex, hence difficult to use efficiently. Also, the amount of on-line information available to a user through Internet is huge and is still increasing everyday so that recovering information is becoming harder and harder. Finally, together with the huge development of Internet, more and more on-line commercial websites and services are proposed to Internet users. The aim and interest of user modeling consists in these situations in helping the user to efficiently use the systems he is offered and to retrieve the information he is looking for by filtering the information according to his will and needs. Furthermore, while many software, hypermedia, websites and services are potentially used by a variety of users, these systems have been traditionally developed in a “one size fits all” manner. Consequently, they are often not adapted to most of the users, with various knowledge, preferences, and needs. In this context user modeling allows personalizing such systems, their content or presentation, in order to fit the individual.

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Slides
0:00 User Modeling and Machine Learning: A Survey
0:26 User Modeling and Machine Learning: A Survey
2:27 Outline
3:04 Main Applications
3:22 Intelligent User Interfaces
5:18 Intelligent User Interfaces
6:34 Adapative Hypermedia (1)
8:53 Adapative Hypermedia (2)
10:53 Adapative Hypermedia (3)
12:04 Adapative Hypermedia (4)
14:04 Educational Systems
14:51 Recommender Systems (1)
16:12 Recommender Systems (2)
21:50 WebSite log analysis –Web analytics solutions
23:47 Anaweb project
26:14 Personalized Information Retrieval (1)
28:50 Personalized Information Retrieval (2)
29:40 Example (1)
30:10 Personalized Information Retrieval (2)
30:20 Example (2)
30:23 Prediction (of Next Action)
32:48 Navigation Help Systems (1)
33:37 Navigation Help Systems (2)
35:25 Navigation Help Systems (3)
35:32 Navigation Help Systems (3)
35:36 Desktop User Help System
35:41 Office Activity Help Systems
37:12 User models (1)
38:09 User models (2)
38:59 User models (3)
39:16 User models (4)
40:26 User models (5)
40:29 Some more details about…
40:32 Web log preprocessing (1)
41:53 Web log preprocessing (2)
42:51 Web log preprocessing (3)
44:25 Web log preprocessing (4)
45:13 Standard Web Usage Mining technique: Association rules
46:17 - Questions
48:40 Standard Web Usage Mining technique: Association rules
49:54 Dealing with sequences
50:05 User navigation behaviour detection and tracking (1)
52:33 User navigation behaviour detection and tracking (2)
53:16 User navigation behaviour detection and tracking (3)
53:57 User navigation behaviour detection and tracking (4)
55:15 User navigation behaviour detection and tracking (5)
56:39 User navigation behaviour detection and tracking (6)
57:51 User navigation behaviour detection and tracking (5)
58:23 User navigation behaviour detection and tracking (6)

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