Tuesday, April 4, 2023 - 10:30 to 12:00
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present…
MSc Thesis Defense by: Lama Khalil
Date: Tuesday April 4, 2023
Location: Essex Hall, Room 122
Reminders: 1. Two-part attendance mandatory (sign-in sheet, QR Code) 2. Arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED. Note that due to demand, if the room has reached capacity, even if you are "early" admission is not guaranteed.
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Advances in multi-task learning (MTL) models have improved the performance and explainability of recommender systems (RS) by jointly learning the recommendation and knowledge graph completion (KGC) tasks. Recent studies have established that considering the incomplete nature of knowledge graphs (KG) can further enhance the performance of RS. However, most existing MTL models depend on translation-based knowledge graph embedding (KGE) methods for KGC, which cannot capture various relation patterns, including composition relations that are prevalent in real-world KG. To address this limitation, this thesis proposes a new MTL model, named rotational knowledge-enhanced translation-based user preference (RKTUP). RKTUP enhances the KGC task by incorporating rotational-based KGE techniques (RotatE or HRotatE) to model and infer diverse relation patterns. These relation patterns include symmetry/asymmetry, composition, and inversion. RKTUP is an advanced variant of the knowledge-enhanced translation-based user preference (KTUP) MTL model, which provides interpretations of its recommendations. The experimental results demonstrate that RKTUP outperforms existing methods and achieves state-of-the-art performance on both recommendation and KGC tasks. Specifically, it shows a 13.7\% and 11.6\% improvement in F1 score for recommendations on DBbook2014 and MovieLens-1m, respectively, and a 12.8\% and 13.6\% increase in hit ratio for KGC on the same datasets, respectively. The use of RotatE improves the two tasks' performance, while HRotatE enhances the two tasks' performance and the model's efficiency.
Keywords: Recommender System, Knowledge Graph Completion, Multi-Task Learning Model.
MSc Thesis Committee:
Internal Reader: Dr. Kalyani Selvarajah
External Reader: Dr. Mohammad Hassanzadeh
Advisor: Dr. Ziad Kobti
Chair: Dr. Adel Abusitta
MSc Thesis Defense Announcement
5113 Lambton Tower 401 Sunset Ave. Windsor ON, N9B 3P4 (519) 253-3000 Ext. 3716 firstname.lastname@example.org