A smartphone-based activity-aware system for music streaming recommendation

Lee, Wei‐Po; Chen, Chun‐Ting; Huang, Jhih-Yuan; Liang, Jhen-Yi

doi:10.1016/j.knosys.2017.06.002

Cited by 29 publications

(15 citation statements)

References 28 publications

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“…The environment-related contexts include temporal contexts [15], geographical contexts [16], and so on. The userrelated contexts include activity [17], emotional state [14], and so on. Hybrid methods [8] try to alleviate the influence of data sparsity by integrating different recommendation strategies and usually obtain better results.…”

Section: A Music Recommendationmentioning

confidence: 99%

CAME: Content- and Context-Aware Music Embedding for Recommendation

Wang

Zhang

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions. Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. Then, a novel method called content-and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN. Especially, one music piece generally highlights different aspects when interacting with various neighbors, and it should have different representations separately. CAME seamlessly combines deep learning techniques, including convolutional neural networks and attention mechanisms, with the embedding model to capture the intrinsic features of music pieces as well as their dynamic relevance and interactions adaptively. Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content-and context-aware music recommendation method. Comprehensive experiments as well as quantitative and qualitative evaluations have been performed on real-world music data sets, and the results show that the proposed recommendation approach outperforms stateof-the-art baselines and is able to handle sparse data effectively.

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Section: A Music Recommendationmentioning

confidence: 99%

CAME: Content- and Context-Aware Music Embedding for Recommendation

Wang

Zhang

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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“…Some classes were not combined when the features were integrated. Authors in [16] have improved the extraction of contextual information by continuous monitoring of the past historical data. More works have been focussed using deep learning networks [17] which especially helped to detect the malicious users [18].…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Book Recommendation System using Apriori & FP Growth Algorithm Removing the Barriers of Time & Memory Constraint.

Dey

Sharma

2021

Preprint

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Rapid advancements made in the technologies has increased the growth of the information on the internet. It becomes a challenging process for the users to suggest a right decision at the right time. The real-time issues are explored by the recommender systems. It is observed that some items are not classified properly and thus leads to improper recommendation processes under different context. Henceforth, the enhancement of the contextual information will improve the performance of the recommendation system. This paper is an enhancement of the recommendation system for book management applications. We have proposed FP-Growth algorithm that recommends the books to the user’s interest. Since the apriori algorithm scans the transactional database several times, it would lead to an improper recommendation process. Thus, frequent pattern mining is employed here, to extract the frequent patterns. These patterns are then stored in the frequent lists. Based on the user’s query, then the relevant books are recommended. The proposed algorithm is analyzed over a book dataset collected from codeproject.com. The frequently rated books are extracted and patterns are stored in the transactional database of apriori algorithm. The proposed mining algorithm is analyzed using performance metrics such as accuracy, precision, recall and f-1score. The results have proved the effectiveness of the algorithm by improving the recommendation accuracy and reduced retrieval time. The execution time of Apriori algorithm is 35ms whereas FP growth is 10 ms.

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“…These rapid advances in context acquisition, representation and adaptation have triggered many context-aware LBS, such as for navigation and wayfinding (Raubal and Panov 2009), location recommendations and mobile guides (Huang 2016), mobile learning (i.e. learning experiences delivered via mobile devices) (Gómez et al 2014), healthcare (Ham, Dirin, and Laine 2017), and entertainment (Lee et al 2017).…”

Section: Towards Context-awarenessmentioning

confidence: 99%

Location based services: ongoing evolution and research agenda

Huang

Gärtner

Krisp

et al. 2018

Journal of Location Based Services

244

132

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We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. This article first reviews the ongoing evolution and research trends of the scientific field of LBS in the past years. To motivate further LBS research and stimulate collective efforts, this article then presents a series of key research challenges that are essential to advance the development of LBS, setting a research agenda for LBS to 'positively' shape the future of our mobile information society. These research challenges cover issues related to the core of LBS development (e.g. positioning, modelling, and communication), evaluation, and analysis of LBS-generated data, as well as social, ethical, and behavioural issues that rise as LBS enter into people's daily lives.

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A smartphone-based activity-aware system for music streaming recommendation

Cited by 29 publications

References 28 publications

CAME: Content- and Context-Aware Music Embedding for Recommendation

CAME: Content- and Context-Aware Music Embedding for Recommendation

A Comparative Book Recommendation System using Apriori & FP Growth Algorithm Removing the Barriers of Time & Memory Constraint.

Location based services: ongoing evolution and research agenda

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A smartphone-based activity-aware system for music streaming recommendation

Cited by 29 publications

References 28 publications

CAME: Content- and Context-Aware Music Embedding for Recommendation

CAME: Content- and Context-Aware Music Embedding for Recommendation

A Comparative Book Recommendation System using Apriori &amp; FP Growth Algorithm Removing the Barriers of Time &amp; Memory Constraint.

Location based services: ongoing evolution and research agenda

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Product

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A Comparative Book Recommendation System using Apriori & FP Growth Algorithm Removing the Barriers of Time & Memory Constraint.