Harnessing Latent Space Semantics for Enhanced Interpretability of Recommender Systems in Item Retrieval and Online Communication Dynamics

Donkers, T. (2024). [PhD thesis].


Recommender systems, in today’s digital age, have emerged as influential algorithmic curators, significantly shaping content consumption, e-commerce, and public opinion. While the majority of research in this area has been anchored in algorithm development and offline evaluation, user-centered considerations are still conspicuously neglected. In particular, pervasive technologies, while powerful, tend to operate as inscrutable \backslashemph{black boxes}, concealing their inner workings from both developers and end users. By exploring the potential for improving the interpretability of the latent information spaces employed by many recommendation models, this work emphasizes the importance of understanding the structural conditions that form the underlying data base. It highlights the depth of insight that can be gained from the intricate relationships between the entities under consideration, and aims to bridge the current gaps in our understanding of recommender systems. The first goal of this thesis is to advance model-based recommender systems by harnessing the power of latent information spaces. To this end, two novel methods are introduced: Tag-enhanced Matrix Factorization (TagMF) and Aspect-based Transparent Memories (ATM). TagMF extends traditional matrix factorization by intertwining associations between items and tags, thereby indirectly inferring user-tag relationships as well. This not only enriches the user-item preference matrix, improving prediction accuracy and mitigating data sparsity issues, but also increases the degree to which the otherwise latent semantics can be interpreted. On the other hand, ATM leverages user reviews and applies deep learning techniques to provide evidence-backed recommendations. By associating the semantic subtleties within its latent space with concrete user utterances, ATM paves the way for transparent recommender systems that more closely resemble how humans justify their evaluations of items. Together, these approaches, validated by user studies, enable users to interactively navigate and influence the recommendation process, increasing both perceived self-efficacy and recommendation quality. The second objective introduces a methodology grounded in simulation, offering a nuanced lens to comprehend social phenomena pertinent to recommender systems in online social networks. Recognizing the gaps in existing research, this approach underscores the significance of psychological and sociological factors in deciphering the impact of these systems. Traditional offline evaluations, predominantly centered on predicting item ratings or rankings, often bypass the diverse influences on decision-making within recommendation tasks. While user-centric experimental studies have sought alignment between recommendation technology and psychological or demographic attributes, they tend to narrow down phenomena to individual experiences. In environments like social networks, where collective dynamics are crucial, broader effects must be considered to fully grasp the societal implications of recommender systems. To bridge these research voids, our methodology harnesses the rich semantic connections inherent in latent information spaces. It seeks to analyze phenomena such as the polarization of ideological groups by tracing the evolution of these semantics. This perspective allows us to understand how the dynamics between individual users, their social environment, and algorithm-driven content distribution together influence the spread of opinions and the manifestation of particular beliefs within online networks. In summary, this thesis presents an alternative approach to studying recommender systems that emphasizes human factors and latent semantics. It argues that, beyond predicting ratings or ranking alternatives, additional information needs can be satisfied by accessing and disclosing latent semantics inferred by contemporary machine learning technologies, ultimately enabling users to properly evaluate system-side suggestions. In addition, the work proposes a simulation paradigm to reconcile psychological and sociological factors influencing decision-making and communication behavior with variations in recommendation technology. These contributions aim to improve our understanding of the complex relationship between users and recommender systems, ultimately enhancing their functionality and impact.


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