Hey guys! My name is Julia and I am last year comp sci student at UNC Charlotte, going to Northeastern in Fall 2025 for grad school 
My major area of research during undergraduate studies was in data visualization, particularly in the context of explainability to users and in the context of social computing: how do users interact with data and share data that they receive in visualizations? That being said, I also have interest in recommendation systems and how that information is displayed to users.
Recommendation systems typically fall into two categories; collaborative recommendation systems and content-based recommendation systems. We can observe the collaborative paradigm in applications like Spotify or Apple Music, which both allow users to exchange their listening data with friends, and both recommend music based on the traditional āshared interests between usersā. My major focus this past semester has been on content-based recommendation systems, and novel approaches to these systems.
This is where I believe my involvement in development can be beneficial to ListenBrainz. I propose a novel content-based recommendation system that is informed by semantics embedded within album descriptors, album reviews, and album cover art. To further break this down I will outline how I envision such a system working, and how these three inputs will allow for serendipitous recommendations for users.
A recommendation system typically benefits from having some semblance of what a user already enjoys (this is to solve the ācold-startā problem where a system doesnāt know where to begin recommending). Since ListenBrainz already has this feature by allowing users to import previous data, the main focus then shifts towards recommendations, particularly content-based recommendations. Commercially available albums can be viewed through the lens of ācorrelationsā between actual musical content, and what a user wants to listen to. Tags are the most common form of this correlation and they are something that has been beaten to death within environments such as Spotify. One of the most common issues that arises from this is a filter bubble where a user is trapped within an echo chamber essentially. A solution to this issue could be the introduction of serendipity through various new means.
The idea here is to rely on existing information from publicly available digital libraries such as wikipedia, and to extract hyperspecific features instead of relying on short lists of tags that may be too vague. Based on review language descriptions, new and insightful semantics can be extracted which can offer richer mood based descriptions of a particular album. We can combine these parameters with yet another point of metadata that often goes overlooked in album recommendations; album art. Album art is but an extension of an album. It offers a completely new dimension of semantics that can help users understand just what exactly theyāre getting into. A great example could be the 2000ās digital minimalism of brat which evokes emotions of nostalgia, envy, and general nonchalantness. Perhaps we see the cover of āThe Dark Side of the Moonā and weāre filled with a sense of inexplicable awe. With a computer vision approach, we can attempt quantify these semantics through the use of basic visual design elements such as color, shapes, value, texture, but also visual design principles such as unity, hierarchy, contrast, and dominance. If we combine these features with semantics derived from text, and opt to offer an explainable approach for recommendations we can help offer users a sense of agency they might feel is lost in the age of artificial intelligence. This will encourage users to explore albums as an art, and can encourage users to break out of filters they exist in.
Please let me know if you have any questions, or any feedback for this idea!
. My initial idea was to scrape sentiments from critic reviews off of Wikipedia using NLP methods, due to how much information that can provide.