论文信息 - Tribal taste: mobile multiagent recommender system

Tribal taste: mobile multiagent recommender system

We demonstrate a system for filtering media streams according to the collective taste of a leader-less informal clan of users. Applications on mobile devices receive streams of content items that are assessed by local software agents. The agents learn the collective preferences of the tribe by forming a distributed multi-agent society that shares data on the behavior of all users. The underlying artificial intelligence is based on support vector machines that cooperate by broadcasting new support vectors. The demo shows micro-blog readers on cell phones running support vector machine agents with text kernels and communicating over IP Multimedia System networks.

Magnus Jändel | Mehdi Elahi | M. Jändel | Mehdi Elahi

[1] Jun Wang,et al. Distributed collaborative filtering for peer-to-peer file sharing systems , 2006, SAC.

[2] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[3] Magnus Jändel. Cooperating Classifiers , 2008, NICSO.

[4] Hsinchun Chen,et al. Design and evaluation of a multi-agent collaborative Web mining system , 2003, Decis. Support Syst..

[5] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[6] Gonzalo Camarillo,et al. The 3G IP multimedia subsystem , 2006 .

[7] Bernhard Schölkopf,et al. An Introduction to Support Vector Machines , 2003 .

[8] Nello Cristianini,et al. An introduction to Support Vector Machines , 2000 .

[9] Thorsten Joachims,et al. Text categorization with support vector machines , 1999 .