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Li, Dandan; Kwong, C. K.; Lee, Dik Lun

doi:10.1002/asi.v61:1

Cited by 134 publications

(4 citation statements)

References 504 publications

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“…On the other hand, approaches that address user experience (UX) can be investigated to evaluate recommender systems. For example, a MRS can be evaluated based on user engagement, which provides a restricted explanation of UX that concentrates on judgment of product quality during interaction [80,118,133]. User satisfaction, user engagement, and more generally user experience are commonly assessed through user studies [14,116,117].…”

Section: Challenge 3: Evaluating Music Recommender Systemsmentioning

confidence: 99%

Current challenges and visions in music recommender systems research

Schedl

Zamani

Chen

et al. 2018

Int J Multimed Info Retr

250

155

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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 ngertip. While today's MRS considerably help users to nd 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. e purpose of this trends and survey article is twofold. We rst 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 eld. e 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 eld.

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Section: Challenge 3: Evaluating Music Recommender Systemsmentioning

confidence: 99%

Current challenges and visions in music recommender systems research

Schedl

Zamani

Chen

et al. 2018

Int J Multimed Info Retr

250

155

View full text Add to dashboard Cite

show abstract

“…The engagement was positively evaluated through the analysis of various parameters [79] such as: perceived usability, felt involvement, focused attention, aesthetics, novelty and endurability. The educational impact was measured some months after the end of the pedagogical path and revealed improvements in all the areas involved by the multidisciplinary experience.…”

Section: Data Collectionmentioning

confidence: 99%

“…5.4.7 and (Appendix C) was composed mainly of closed questions addressed to analyse different facets of the pupils' experience. We analysed the six different parameters that define the user engagement according to O'Brien et al [79]: perceived usability, felt involvement, focused attention, aesthetics, novelty and endurability. This analysis is useful for measuring all those experiences that go beyond the working activity and whose success is also determined by parameters such as aesthetics or felt involvement.…”

Section: Nd Questionnaire: the Experiencementioning

confidence: 99%