Tag-aware dynamic music recommendation

Zheng, Ervine; Kondo, Gustavo Yukio; Zilora, Stephen J.; Yu, Qi

doi:10.1016/j.eswa.2018.04.014

Cited by 48 publications

(22 citation statements)

References 7 publications

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“…Ferwerda et al (2015) used a specific personality-enriched dataset that provided links to users' listening histories on Last.fm to leverage personality traits to predict a user's genre preferences. Zheng et al (2018) proposed a tag-aware dynamic music recommendation framework that represents musical tracks via user-generated tags and generates time-sensitive recommendations. Koenigstein et al (2011) incorporated a temporal analysis of user ratings assigned to music pieces and item popularity trends into a matrix factorization approach to mitigate the issue of insufficient item ratings.…”

Section: Temporal Dynamics Of Music Preferencesmentioning

confidence: 99%

Modeling Popularity and Temporal Drift of Music Genre Preferences

Lex¹,

Kowald²,

Schedl

2020

Transactions of the International Society for Music Information Retrieval

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In this paper, we address the problem of modeling and predicting the music genre preferences of users. We introduce a novel user modeling approach, BLL u , which takes into account the popularity of music genres as well as temporal drifts of user listening behavior. To model these two factors, BLL u adopts a psychological model that describes how humans access information in their memory. We evaluate our approach on a standard dataset of Last.fm listening histories, which contains fine-grained music genre information. To investigate performance for different types of users, we assign each user a mainstreaminess value that corresponds to the distance between the user's music genre preferences and the music genre preferences of the (Last.fm) mainstream. We adopt BLL u to model the listening habits and to predict the music genre preferences of three user groups: listeners of (i) niche, low-mainstream music, (ii) mainstream music, and (iii) medium-mainstream music that lies in-between. Our results show that BLL u provides the highest accuracy for predicting music genre preferences, compared to five baselines: (i) group-based modeling, (ii) user-based collaborative filtering, (iii) item-based collaborative filtering, (iv) frequency-based modeling, and (v) recencybased modeling. Besides, we achieve the most substantial accuracy improvements for the low-mainstream group. We believe that our findings provide valuable insights into the design of music recommender systems.

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Section: Temporal Dynamics Of Music Preferencesmentioning

confidence: 99%

Modeling Popularity and Temporal Drift of Music Genre Preferences

Lex¹,

Kowald²,

Schedl

2020

Transactions of the International Society for Music Information Retrieval

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show abstract

“…Qian et al [26] fused three types of representative heterogeneous information to comprehensively analyze user features, such as ratings, user social networks, and user review sentiments. Zheng et al [27] considered the evolving nature of user preferences over time and developed a time-sensitive and tag-aware recommendation framework. Bougiatiotis and Giannakopoulos [28] presented a contentbased movie recommender system that was based on textual information and audio and visual channels.…”

Section: Additional Data Sources For Recommender Systemsmentioning

confidence: 99%

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

Gan

Zhang

2020

Complexity

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Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade. Learning effective latent factors plays the most important role in recommendation methods. Several recent works extracted latent factors from user-generated content such as ratings and reviews and suffered from the sparsity problem and the unbalanced distribution problem. To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata. In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items. Experiments conducted on the Amazon product data demonstrate that our approach outperforms multiple state-of-the-art baselines. We provide further insight into the design selections and hyperparameters of our recommendation method. In addition, we further explore the relative importance of various item metadata information on improving the rating prediction performance towards personalized product recommendation, which is extremely valuable for feature extraction in practice.

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“…Conventionally, MF that can estimate users' preference from their operation histories has been proposed [9]- [11], [36]- [43]. Specifically, singular value decomposition (SVD), for which the input is a user-item rating matrix and output is latent factors of users' ratings [11], [39], was utilized for musical piece recommendation.…”

Section: A Musical Piece Recommendation and Video Recommendationmentioning

confidence: 99%

Music Video Recommendation Based on Link Prediction Considering Local and Global Structures of a Network

et al. 2019

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A novel method for music video recommendation is presented in this paper. The contributions of this paper are twofold. (i) The proposed method constructs a network, which not only represents relationships between music videos and users but also captures multi-modal features of music videos. This enables collaborative use of multi-modal features such as audio, visual, and textual features, and multiple social metadata that can represent relationships between music videos and users on video hosting services. (ii) A novel scheme for link prediction considering local and global structures of the network (LP-LGSN) is newly derived by fusing multiple link prediction scores based on both local and global structures. By using the LP-LGSN to predict the degrees to which users desire music videos, the proposed method can recommend users' desired music videos. The experimental results for a real-world dataset constructed from YouTube-8M show the effectiveness of the proposed method.

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Tag-aware dynamic music recommendation

Cited by 48 publications

References 7 publications

Modeling Popularity and Temporal Drift of Music Genre Preferences

Modeling Popularity and Temporal Drift of Music Genre Preferences

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

Music Video Recommendation Based on Link Prediction Considering Local and Global Structures of a Network

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