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   Together Yet Apart: Multimodal Representation Learning for Personalised Visual Art RecommendationYilma, Bereket Abera  ; Leiva, Luis A. in Yilma, Bereket Abera; Leiva, Luis A. (Eds.) Proceedings of the ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2023) (2023, June 26) With the advent of digital media, the availability of art content has greatly expanded, making it increasingly challenging for individuals to discover and curate works that align with their personal ... [more ▼] With the advent of digital media, the availability of art content has greatly expanded, making it increasingly challenging for individuals to discover and curate works that align with their personal preferences and taste. The task of providing accurate and personalised Visual Art (VA) recommendations is thus a complex one, requiring a deep understanding of the intricate interplay of multiple modalities such as images, textual descriptions, or other metadata. In this paper, we study the nuances of modalities involved in the VA domain (image and text) and how they can be effectively harnessed to provide a truly personalised art experience to users. Particularly, we develop four fusion-based multimodal VA recommendation pipelines and conduct a large-scale user-centric evaluation. Our results indicate that early fusion (i.e, joint multimodal learning of visual and textual features) is preferred over a late fusion of ranked paintings from unimodal models (state-of-the-art baselines) but only if the latent representation space of the multimodal painting embeddings is entangled. Our findings open a new perspective for a better representation learning in the VA RecSys domain. [less ▲] Detailed reference viewed: 175 (28 UL)The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of PaintingsYilma, Bereket Abera ; Leiva, Luis A. in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23) (2023, April) Artwork recommendation is challenging because it requires understanding how users interact with highly subjective content, the complexity of the concepts embedded within the artwork, and the emotional and ... [more ▼] Artwork recommendation is challenging because it requires understanding how users interact with highly subjective content, the complexity of the concepts embedded within the artwork, and the emotional and cognitive reflections they may trigger in users. In this paper, we focus on efficiently capturing the elements (i.e., latent semantic relationships) of visual art for personalized recommendation. We propose and study recommender systems based on textual and visual feature learning techniques, as well as their combinations. We then perform a small-scale and a large-scale user-centric evaluation of the quality of the recommendations. Our results indicate that textual features compare favourably with visual ones, whereas a fusion of both captures the most suitable hidden semantic relationships for artwork recommendation. Ultimately, this paper contributes to our understanding of how to deliver content that suitably matches the user's interests and how they are perceived. [less ▲] Detailed reference viewed: 271 (73 UL)A Contextual Framework for Adaptive User Interfaces: Modelling the Interaction EnvironmentDubiel, Mateusz ; Yilma, Bereket Abera ; Latifzadeh, Kayhan et alScientific Conference (2022) Detailed reference viewed: 103 (22 UL) |
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