Publication Type

Conference Proceeding Article

Publication Date

1-2016

Abstract

This paper presents a personalized event scheduling recom-mender system, PRESS, for a large conference setting with multiple parallel tracks. PRESS is a mobile application that gathers personalized information from a user and recommends talks/demos to be attend. The input from a user include a list of keyword preferences and (optionally) preferred talks. We use the MALLET topic model package to analyze the set of conference papers and classify them based on automatically identified topics. We propose an algorithm to generate a list of recommended papers based on the user keywords and the MALLET topics. An optimization model is then applied to obtain a feasible schedule. The recommended set is matched against the selected papers by the user which we obtained from a survey conducted at AAMAS-15 in Istanbul, Turkey. We show that PRESS is able to provide reasonable accuracy, precision and recall rates. PRESS will be deployed live during AAMAS-16 in Singapore.

Keywords

Conference scheduling, Recommender system, Topic model

Discipline

Software Engineering | Theory and Algorithms

Research Areas

Intelligent Systems and Decision Analytics

Publication

15th International Conference on Autonomous Agents and Multiagent Systems: AAMAS 2016, Singapore, 2016 May 9-13

First Page

1513

Last Page

1514

ISBN

9781450342391

Publisher

International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

City or Country

Singapore

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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