title: Explainable visually-aware recommender systems based on heterogeneous information networks
Creators: name: Markchom, Thanet
Publisher: name: University of Reading
Year: 2024

description: Recommender systems are crucial for addressing information overload by providing users of online platforms, applications, or services with relevant item suggestions. Visually-aware systems
enhance recommendations using images in various domains, e.g., fashion and entertainment.
However, most visually-aware recommender systems lack explainability due to their reliance on
black-box approaches such as deep-learning techniques. This thesis addresses the need for accurate and explainable visually-aware recommender systems by integrating visual information
into Heterogeneous Information Networks (HINs). Three main research questions arise: How
can the effective integration of visual information into HINs be achieved? How can the development of explainable visually-aware recommender systems based on HINs be approached?
What methods can be used to enhance the explainability of HIN-based recommender systems?
To achieve these, this thesis proposes a three-part solution. Firstly, visually-augmented
HINs are constructed by introducing visual factor nodes and relations, generated from various
image features. A user representation learning method is introduced to combine semantic and
visual preferences. Secondly, a Scalable and Explainable Visually-aware Recommender System
(SEV-RS) framework is presented. SEV-RS utilizes meta-paths for explainability, enhancing
recommendations using scalable feature extraction from visually-augmented HINs. Lastly, a
meta-path translation model is introduced to improve the explainability of meta-path based
systems, enhancing the performance of the proposed framework.
Extensive experiments with real-world datasets in movies and clothing domains assessed
the effectiveness of visually-augmented HINs. These augmented HINs, along with the proposed user representation learning method, were employed in a recommendation model using
Collaborative Filtering with K-Nearest Neighbors (CF-KNN). This model was compared with
other CF-KNN models based on regular HINs. The findings indicate the efficacy of visually-augmented HINs and the representation learning approach in enhancing CF-KNN. They also
exhibit the practicality of using visually-augmented HINs in state-of-the-art recommender systems. The evaluation of SEV-RS involved comparisons with state-of-the-art models using
real-world and synthetic datasets. The results reveal that SEV-RS yields recommendations
with high accuracy and explainability while requiring notably less computational time compared
to other deep-learning models. The proposed meta-path translation approach was evaluated
on two newly generated datasets derived from real-world recommendation data. It was compared with various sequence-to-sequence models in the task of translating a given meta-path
to a group of meta-paths with higher explainability. The experiments validate its capability
to generate more comprehensible alternative explanations for complex meta-paths. These
approaches create pathways for developing recommender systems that address users’ visual
preferences and offer explanations based on understandable meta-paths. Implementing these
methods could enhance user experiences, leading to more engaging and personalized recommender systems, with potential adaptability for improving other graph-utilizing explainable
artificial intelligence applications.