Music Recommendation Based on “User-Points-Music” Cascade Model and Time Attenuation Analysis

Wang, Tuntun; Li, Junke; Zhou, Jincheng; Li, Mingjiang; Guo, Yong

doi:10.3390/electronics11193093

Cited by 1 publication

(1 citation statement)

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“…Without regard to whether the length of the music list consumed is short or not, ref. [ 20 ] created a music clustering model to extract the interest points for a music recommendation system without having to predetermine the number of clusters. Bharti et al [ 21 ] developed a model to deliver the best and fastest recommendations by maintaining and clustering current users and items of the system.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization

Kutlimuratov

Abdusalomov

Oteniyazov

et al. 2022

Sensors

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E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset’s sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them. Furthermore, the effectiveness of rating prediction and system customization increases when the customer-added tag information is combined with hierarchically structured hidden implicit features. For these reasons, we concentrate on early grouping of comparable customers using the clustering technique as a first step, and then, we further enhance the efficacy of recommendations by obtaining implicit hidden features and combining them via customer’s tag information, which regularizes the deep-factorization procedure. The idea behind the proposed method was to cluster customers early via a customer rating matrix and deeply factorize a basic WNMF (weighted nonnegative matrix factorization) model to generate customers preference’s hierarchically structured hidden implicit features and product characteristics in each cluster, which reveals a deep relationship between them and regularizes the prediction procedure via an auxiliary parameter (tag information). The testimonies and empirical findings supported the viability of the proposed approach. Especially, MAE of the rating prediction was 0.8011 with 60% training dataset size, while the error rate was equal to 0.7965 with 80% training dataset size. Moreover, MAE rates were 0.8781 and 0.9046 in new 50 and 100 customer cold-start scenarios, respectively. The proposed model outperformed other baseline models that independently employed the major properties of customers, products, or tags in the prediction process.

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Section: Introductionmentioning

confidence: 99%