1 / 13

Artificial Intelligence Techniques

Artificial Intelligence Techniques. Internet Applications 4. Plan for next four weeks. Week A – AI on internet, basic introduction to semantic web, agents. Week B – Microformats Week C – Collective Intelligence and searching 1 Week D – Collective Intelligence and searching 2.

armine
Download Presentation

Artificial Intelligence Techniques

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Artificial Intelligence Techniques Internet Applications 4

  2. Plan for next four weeks • Week A – AI on internet, basic introduction to semantic web, agents. • Week B – Microformats • Week C – Collective Intelligence and searching 1 • Week D – Collective Intelligence and searching 2

  3. Aims of sessions

  4. Ways of using Collective Intelligence 1 • Taken from Alag(2009) • Lists • Create lists generated by users. • Ratings and recommendations (see last week) • From blogs,wikis,etc • Extracted from contributions from users.

  5. Ways of using Collective Intelligence 2 • Tagging, voting, bookmarking* • CI of users can be ‘bubble up’ interesting content • Clustering* • Clustering users and items, predicitive models

  6. Taken from Alag (2009)

  7. Taken from Alag (2009)

  8. Basic CI algorithms and issues • Need common language. • Content-based • Relevance is anchored in the content. • Collaborative • Users’ interaction to discern meaning.

  9. User Profiles • User profiles contain attributes • Can be of different types • Range of same type can be wide. • Not all attributes are equal • Need to normalise data depending on the learning algorithms.

  10. As well as ‘personal’ data, might include for example: • What they clicked on • Average time on a page • Items clicked on • Items purchased

  11. Stemming • Terms and phrases in a document form the representation of the content. • Terms and their associated weightings –term vectors • Similarity of terms is dependent on these term vectors. • How would you do this?

  12. Web2.0 to Web 3.0 • “CI is the core component of Web 2.0”Alag (2009) • Web 3.0 is expect to have artificial intelligence at its core. • Is there a link between CI and Web 3.0?

  13. References • Alag S (2009) Collective Intelligence in Action Manning ISBN 1933988312 • Segaran (2007) Programming collective Intelligence O’Reilly isbn- 0-596-52932-5

More Related