School of Engineering Department of Computer Science and Engineering 127 Efficient Queries over Database Supervisor: WONG Raymond Chi Wing / CSE Student: LU Weiqi / COSC SU Hong / DSCT Course: UROP1100, Fall UROP1100, Fall In nature, some of the objects have the trend to form colocation patterns, which produces a way to study the co-behaviors of different objects. But only studying the colocation patterns is far not enough. Exploring the causality among the colocation patterns boosts endless value in many areas ranging from ecosystem to business. This report proposes a statistical approach to mine causalities among colocation patterns. Efficient Queries over Database Supervisor: WONG Raymond Chi Wing / CSE Student: PATUPAT Albert John Lalim / DSCT Course: UROP3100, Fall Despite several advancements in the field of session-based recommender systems, there is no widely acknowledged assessment framework that employs multiple splits while preventing future information leaking. This report attempted to address this issue by proposing an evaluation protocol with comprehensive instructions and demonstrating its use by assessing three session-based recommendation algorithms based on the k-nearest neighbors approach, namely Item K-Nearest Neighbors (IKNN), Session K-Nearest Neighbors (SKNN), and Sequence and Time Aware Neighborhood (STAN). Results reveal that IKNN is more powerful than previously reported, that SKNN is prone to popularity bias, and that STAN regularly outperforms SKNN. Future work includes applying this proposed evaluation protocol to session-based recommendation algorithms based on recurrent neural networks and graph neural networks.
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