User logs processing using machine learning techniques
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.
| 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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