Carousel Personalization in Music Streaming Apps with Contextual Bandits

Bendada, Walid; Salha, Guillaume; Bontempelli, Théo

doi:10.1145/3383313.3412217

Cited by 39 publications

(34 citation statements)

References 33 publications

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“…As a consequence, it may lead to the selection of algorithms that provide similar sets of recommendations. Since it is known that a set of diverse recommendations improves user satisfaction [3], and recommending the same item in multiple lists has little use, in some cases it will be beneficial to include algorithms with a lower individual recommendation quality if they generate recommendations with a different perspective. Most articles targeting recommendations in a carousel setting are evaluated online with users of a certain platform.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

Felicioni

Dacrema

Cremonesi

2021

Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

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Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels, each built with a specific criterion (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. This is not considered by traditional offline evaluation protocols. Hence, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We report experiments on publicly available datasets on the movie domain and notice that under a carousel setting the ranking of the algorithms change. In particular, when a SLIM carousel is available, matrix factorization models tend to be preferred, while item-based models are penalized. We also propose to extend ranking metrics to the two-dimensional carousel layout in order to account for a known position bias, i.e., users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen.

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Section: Introductionmentioning

confidence: 99%

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

Felicioni

Dacrema

Cremonesi

2021

Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

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“…Various use cases of MRSs exist, centered around different tasks. Among these, the most important ones are front page recommendation (recommending content for thematic collections of music -also known as shelves or channelspresented to the user on the front page of the platform's user interface) [11], music exploration/discovery (e. g., based on item similarity in terms of melody, rhythm, or lyrics) [41,60], automatic playlist generation (commonly based on the user profile, but possibly only based on a seed description such as "music to relax"), and automatic playlist continuation (based on a sequence of seed tracks) [50,117].…”

Section: Common Music Recommendation Tasks and Methodsmentioning

confidence: 99%

“…Many providers of commercial music streaming services design their recommendation interface as swipeable carousels [11], namely sequences of sections that users can scroll. These carousels have titles that convey information to end-users such as: • Self-explanatory titles: e. g., "Top 10", "Popular in your area", "Trending content" or "Recommended for you" that merely indicate the content selection process (Figure 4 top).…”

Section: Explanations In Real Mrsmentioning

confidence: 99%

Explainability in Music Recommender Systems

Afchar¹,

Melchiorre²,

Schedl³

et al. 2022

Preprint

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The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate.While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty.In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders' explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply -or need to be adapted -to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy-based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large-scale industrial music recommender system and provide research perspectives.

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“…• 𝐴 becomes 𝐴 ′ , an (𝑛 + 𝑚) × (𝑛 + 𝑚) adjacency matrix 6 , with 𝑚 new rows and columns filled with zeros. 4 Learning m𝑖 , as defined in equation (1), is equivalent to learning 𝑚 𝑖 , but allows to get rid of the logarithm and of the constant 𝐺 in computations. 5 Formally, Ã = 𝐷 −1 out (𝐴 + 𝐼 𝑛 ) where 𝐼 𝑛 is the 𝑛 × 𝑛 identity matrix and 𝐷 out is the diagonal out-degree matrix [53] of 𝐴 + 𝐼 𝑛 .…”

Section: 𝜇 and 𝑊mentioning

confidence: 99%

Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

Salha-Galvan¹,

Hennequin²,

Chapus³

et al. 2021

Fifteenth ACM Conference on Recommender Systems

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On an artist's profile page, music streaming services frequently recommend a ranked list of "similar artists" that fans also liked.However, implementing such a feature is challenging for new artists, for which usage data on the service (e.g. streams or likes) is not yet available. In this paper, we model this cold start similar artists ranking problem as a link prediction task in a directed and attributed graph, connecting artists to their top-𝑘 most similar neighbors and incorporating side musical information. Then, we leverage a graph autoencoder architecture to learn node embedding representations from this graph, and to automatically rank the top-𝑘 most similar neighbors of new artists using a gravity-inspired mechanism. We empirically show the flexibility and the effectiveness of our framework, by addressing a real-world cold start similar artists ranking problem on a global music streaming service. Along with this paper, we also publicly release our source code as well as the industrial graph data from our experiments.

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Carousel Personalization in Music Streaming Apps with Contextual Bandits

Cited by 39 publications

References 33 publications

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

Explainability in Music Recommender Systems

Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

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