Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Synthese Recommender System
1. A Hybrid, Multi-Dimensional Recommender for Journal Articles in a Scientific Digital Library Andre Vellino [email_address] Canada Institute for Scientific and Technical Information National Research Council http://lab.cisti-icist.nrc-cnrc.gc.ca/ WPRS 07 2 November 2007 David Zeber [email_address] Dept. of Statistics Cornell University
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11. Implicit Preferences Generation In Context Search Terms Full Text Author Keyphrase Journal Abstract Project IR Mode Clickstream User State
12. Multi-Dimensional Ratings Matrix Tom Alice Bob Carol p 1 p 2 p 3 p 4 p 5 p 6 Innovation Information Authority 0.3 0.6 0.3 0.7 0.4 0.7 0.2 G. Adomavicious, R. Sankaranarayanan, S. Sen, A. Tuzhilin, ACM Transactions on Information Systems 2005 Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach 0.7 0.2 0.5
Purpose of the talk – to describe an approach to addressing the problem of recommender systems for a DL The system is a work in progress. In collaboration with David Zeber – Ph.D. student in Statistics @ Cornell
![A Hybrid, Multi-Dimensional Recommender for Journal Articles in a Scientific Digital Library Andre Vellino [email_address] Canada Institute for Scientific and Technical Information National Research Council http://lab.cisti-icist.nrc-cnrc.gc.ca/ WPRS 07 2 November 2007 David Zeber [email_address] Dept. of Statistics Cornell University](https://image.slidesharecdn.com/synthese-recommender-system-1195675343561866-5/85/Synthese-Recommender-System-1-320.jpg)
![Outline of Talk ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)