Izvorni znanstveni članak
https://doi.org/10.24138/jcomss-2020-0011
Efficient Behavior Prediction Based on User Events
Peter Szabo
; George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures, Romania
Bela Genge
; George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures, Romania
Sažetak
In 2020 we have witnessed the dawn of machine learning enabled user experience. Now we can predict how users will use an application. Research progressed beyond recommendations, and we are ready to predict user events. Whenever a human interacts with a system, user events are dispatched. They can be as simple as a mouse click on a menu item or more complex, such as buying a product from an eCommerce site. Collaborative filtering (CF) has proven to be an excellent approach to predict events. Because each user can generate many events, this inevitably leads to a vast number of events in a dataset. Unfortunately, the operation time of CF increases exponentially with the increase of data-points. This paper presents a generalized approach to reduce the dataset’s size without compromising prediction accuracy. Our solution transformed a dataset containing over 20 million user events (20,692,840 rows) into a sparse matrix in about 7 minutes (434.08 s). We have used this matrix to train a neural network to accurately predict user events.
Ključne riječi
behavior prediction, Machine Learning, collaborative filtering, user events, Adam
Hrčak ID:
259386
URI
Datum izdavanja:
30.6.2021.
Posjeta: 845 *