1,146 research outputs found
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Visualization for Recommendation Explainability: A Survey and New Perspectives
Providing system-generated explanations for recommendations represents an
important step towards transparent and trustworthy recommender systems.
Explainable recommender systems provide a human-understandable rationale for
their outputs. Over the last two decades, explainable recommendation has
attracted much attention in the recommender systems research community. This
paper aims to provide a comprehensive review of research efforts on visual
explanation in recommender systems. More concretely, we systematically review
the literature on explanations in recommender systems based on four dimensions,
namely explanation goal, explanation scope, explanation style, and explanation
format. Recognizing the importance of visualization, we approach the
recommender system literature from the angle of explanatory visualizations,
that is using visualizations as a display style of explanation. As a result, we
derive a set of guidelines that might be constructive for designing explanatory
visualizations in recommender systems and identify perspectives for future work
in this field. The aim of this review is to help recommendation researchers and
practitioners better understand the potential of visually explainable
recommendation research and to support them in the systematic design of visual
explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page
Beyond the ranked list: User-driven exploration and diversification of social recommendation
The beyond-relevance objectives of recommender systems have been drawing more and more attention. For example, a diversity-enhanced interface has been shown to associate positively with overall levels of user satisfaction. However, little is known about how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this paper, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users' subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs
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