Crowdsourcing Platform for Collecting Cognitive Feedbacks from Users: A Case Study on Movie Recommender System

Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


The aim of this research is to present a crowdsourcing-based recommendation platform called OurMovieSimilarity (OMS), which can collect and sharecognitive feedbacks from users. In particular, we focus on the user’s cognition patterns on the similarity between the two movies. OMS also analyzes the collected data of the user to classify the user group and dynamic changes movie recommendations for each different user. The purpose of this is to make OMS interact intelligently and the data collected faster and more accurately. We received more than a thousand feedbacks from 50 users and did the analyzes this data to group the user. A group of the users can be dynamically changed, with respect to the selection of each user. OMS now still online and collecting data. We have been trying to enrich the cognitive feedback dataset including more than 20,000 feedbacks from 5000 users, so that the recommendation system can make more accurate analysis of user cognitive in choosing the movie similarity.



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2B4010774).


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Authors and Affiliations

  1. 1.Department of Computer EngineeringChung-Ang UniversitySeoulKorea

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