MSc Thesis Defense Announcement of Lama Khalil:"Performance Enhancement of Unified Recommendation and Knowledge Graph Completion Learning by Relation Rotation "

 

Date: Tuesday April 4, 2023

Time:  10:30am-12:00pm

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.

3. Please be respectful of the presenter by NOT knocking on the door for admittance once the door has been closed whether the presentation has begun or not (If the room is at capacity, overflow is not permitted (ie. sitting on floors) as this is a violation of the Fire Safety code).

4. Be respectful of the decision of the advisor/host of the event if you are not given admittance. The School of Computer Science has numerous events occurring soon.

 

 

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.  

 

 

Internal Reader: Dr. Kalyani Selvarajah         

External Reader: Dr. Mohammad Hassanzadeh          

Advisor: Dr. Ziad Kobti 

Chair:     Dr. Adel Abusitta