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(SCC 2020) Collaborative Learning using LSTM-RNN for Personalized Recommendation

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Abstract: Today, the ability to track users' sequence of online activities, makes identifying their evolving preferences for recommendation practicable. However, despite the myriad of available online activity information, most existing time-based recommender systems either focus on predicting some user rating, or rely on information from similar users. These systems, therefore, disregard the temporal and contextual aspects of users preferences, revealed in the rich and useful historical sequential information, which can potentially increase recommendation accuracy. In this work, we consider such rich, user online activity sequence, as a complex dependency of each user's consumption sequence, and combine the concept of collaborative filtering with long short-term memory recurrent neural network (LSTM-RNN), to make personalized recommendations. Specifically, we use encoder-decoder LSTM-RNN, to make sequence-to-sequence recommendations. Our proposed model builds on the strength of collaborative filtering while preserving individual user preferences for personalized recommendation. We conduct experiments using Movielens dataset to evaluate our proposed model and empirically demonstrate that it improves recommendation accuracy when compared to state-of-the-art recommender systems.

Authors: Benjamin A Kwapong, Richard Anarfi and Kenneth K Fletcher (University of Massachusetts Boston, USA)

Email: benjamin.kwapong001@umb.edu, richard.anarfi001@umb.edu, kenneth.fletcher@umb.edu

Who can view: Everyone
Webinar Price: Free
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Benjamin Kwapong is an engineer-turned-computer scientist who is currently a computer science PhD candidate at the University of Massachusetts (Umass) Boston. Benjamin is passionate about exploring the advantages of using data and algorithms to solve real life issues and to provide meaningful predictions of future occurrences. His research focuses on knowledge graphs and graph neural networks for recommender systems. As a Graduate Teaching Assistant at Umass Boston, Benjamin has assisted, guided and mentored other students in the principles of software engineering as well as computer programming languages such as python, C and C++. As a Research Assistant at Umass Boston (services computing lab), Benjamin has used computer vision and artificial intelligence approaches to build recommender systems and error detection models in 3D printing simulations. Benjamin has a bachelor's degree in Computer Engineering from the Kwame Nkrumah University of Science and Technology in Ghana. When he is not working, Benjamin likes to listen to music, dance, and sometimes watch movies.
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