The potential of social-aware multimedia prefetching on mobile devices

Wilk, Stefan; Rückert, Julius; Thram, Timo; Koch, Christian; Effelsberg, Wolfgang; Hausheer, David

doi:10.1109/netsys.2015.7089081

Cited by 4 publications

(1 citation statement)

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“…While it is easy to predict that a user will likely watch the next episode of a TV series on Netflix, for example, this does not hold in the case of music consumption. In fact, several studies have shown poor performance of approaches based on the user's subscription status and global item popularity [Koch et al 2017b;Wilk et al 2015Wilk et al , 2016.…”

Section: Improving Scalability and Efficiencymentioning

confidence: 99%

Content-driven Music Recommendation: Evolution, State of the Art, and Challenges

Deldjoo¹,

Schedl²,

Knees³

2021

Preprint

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The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data -which we refer to as content-driven models -have been replacing pure CF or CB models.In this survey, we review 47 articles on content-driven music recommendation. Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, usergenerated content, and derivative content. We provide a detailed characterization of each category along several dimensions. Second, we identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start. Each article addressing one or more of these challenges is categorized according to the content layers of our onion model, the article's goal(s), and main methodological choices. Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies. Finally, we provide our personal selection of the persisting grand challenges, which are still waiting to be solved in future research endeavors.

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Section: Improving Scalability and Efficiencymentioning

confidence: 99%