A Mobile-Based System for Context-Aware Music Recommendations

Abstract

Abstract. As mobile devices are always with users and music listening is a very personal and situational behaviour, contextual information could be used to greatly enhance music recommendations. However, making such use of context, while learning user profiles, is still a challenging problem. We present a system for collecting context and usage data from mobile devices, but targeted at recommending music according to learned user profiles and specific situations. The developed data flow system requires supporting both short enough response times and longer asynchronous reasoning on the collected data. Furthermore, the mobile phone acts not only as sensor, but is directly related to the effectiveness of the music service experience. Thus, this paper provides a description of our approach to the system and the initial results of a usability test of the mobile application and its backend system.

Analyzing the longitudinal impact of proximity, location, and personality on smartphone usage

“…The net result is a seemingly unrelenting demand for data that places tremendous strain on the existing wireless infrastructure [2]. http://www.computationalsocialnetworks.com/content/1/1 /6 While the process of observing cellular data traffic has gained in popularity significantly in the research community [3][4][5], the extraction of useful design parameters requires an understanding that can only be gained by observing the overall context of usage. Notably, it is the device usage as impacted by non-smartphone aspects including internal factors (goals, personality) and external factors (friendship, environment, nearby users) that are difficult to monitor but yet potentially quite informative [5,6].…”

Considering emotions and contextual factors in music recommendation: a systematic literature review

“…Mobile sensors or social networks could identify user behavior. Cheng and Shen [25] UH,CCI X 10 Helmholz et al [46] SEN, CCI X 11 Hong et al [49] UH, CCI, FEED X 12 Teng et al [117] MDI, UA, CCI, ML, CF X 13 Okada et al [96] ML, UB, MD X 14 Karlsson et al [67] UH, MDI X 15 Nirjon et al [95] SEN, UA, MDI X 16 Wang et al [126] MDI, UA, MD, ML X 17 Miller et al [88] CCI, MD X 18 Chang et al [21] MD, ML -19 Dias et al [32] UA, MD -20 Hansen et al [44] UH, CCI, ML -21 Wang et al [124] CF, MC, MD, UB -22 Assuncao and Neris [7] DIM, SI, MD Emotion X 23 Kang and Seo [66] DIM, MDI, CCI X 24 Abdul et al [2] DISC, SI, SIM, ML X 25 Chiu and Ko [26] DISC, SEN, ML, SI X 26 Iyer et al [57] SEN, FE X 27 Rosa et al [101] DIM, SN X 28 Guo et al [41] SEN, SIM X 29 Lehtiniemi and Holm [79] DIM, SI X 30 Helmholz et al [47] SI, DIM, DISC X 31 Andjelkovic et al [5] DIM, MD,SIM -32 Lopes et al [84] DISC, ML -33 Li and Liu [80] UB, MP -34 Padovani et al [97] DISC, SI, ML -35 Gilda et al [39] DISC, FE, MD ML -36 Vateekul et al [120] DIM, CF, SIM -37 Narducci et al [93] DISC, SN -…”

“…Karlsson et al [67] present a system to collect contextual data to recommend music according to learned user-profiles and specific situations. The authors suggest a context with pre-defined songs and then use the first user interaction and their interaction response to shape another context.…”

“…Several proposed recommendation approaches are evaluated by measuring the accuracy of the correct recommendations. However, Karlsson et al [67] conducted a usability test with active music listeners to collect preferences, needs and opinions on possible improvements to the proposed recommendation system. The preliminary test showed that users felt confused by the lack of direct control over which songs were played and were curious to know which song would be the next suggested.…”

ContextPlayer

“…More details can be found elsewhere [Karlsson et al 2012]. As this case study focuses on the mobile app, the contextual backend platform (CBP) is omitted.…”