Date of Award


Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)


Computer Science

First Advisor

Kenneth Fletcher

Second Advisor

Dan Simovici

Third Advisor

Bo Sheng


Recommender systems have been developed at different levels with different approaches to help resolve the challenge of choice making due to the abundance and variety of information. The aim however remains the same — to provide accurate recommendations for users. Traditional approaches to recommender systems have mainly focused on collaborative filtering or content-based filtering. More recent approaches have explored hybrid models which combine the collaborative and content-based filtering methods. They however fall short in addressing the issues of personalization, cold start, diversity and explainability in recommender systems.

This thesis intends to address these shortfalls by utilizing the advances in Natural Language Processing and Machine Learning.

First, in order to personalize users' recommendations, it is essential to consider their personalized preferences on non-functional attributes. However, to increase recommendation accuracy, it is essential that recommendation systems include users' evolving preferences. Existing recommendation systems fail to thoroughly capture users' dynamic preferences for personalized recommendation. This work proposes a method to personalize users' recommendations based on their dynamic preferences on non-functional attributes.

Secondly, we use side information to resolve cold start and data sparsity limitations in RS, in order to improve their accuracy. Knowledge graphs (KGs) have shown to be very valuable source of side information because they allow hybrid graph-based recommendation methods comprising both collaborative and content information. Using KGs as side information also helps us to find latent connections between the entities in a dataset, to improve recommendation precision and bring explainability in recommendations.

Finally, with the use of transformer pre-trained models, we introduce the ELECTRA-KG (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) which utilizes a custom KG built with the existing items in a data base and treats entities, relations and triples as textual sequences thereby turning KG completion into a sequence classification problem. It uses the description of the entities together with their relations in the KG to compute a scoring function of the triples. By doing so, we can determine the plausibility of a triple or a relation from our fine-tuned model. Experiments and evaluations on real world data shows the effectiveness and accuracy of our proposed models.


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