A Hybrid Recommender System for Improving Automatic Playlist Continuation

Gatzioura, Anna; Vinagre, João; Jorge, Alípio Mário; Sànchez–Marrè, Miquel

doi:10.1109/tkde.2019.2952099

Cited by 17 publications

(12 citation statements)

References 32 publications

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“…In hybrid approaches, predictions are performed on each method involved in the hybridization process separately, and thereafter combined to overcome some common problems such as cold start problem and data sparseness [39]. In the literature, several approaches [40]- [42] are developed to benefit from hybrid recommender systems. The main advantage of those methods is their ability to employ plural aspects raised by interactions between users and items to refine as most as possible the recommendation.…”

Section: Hybrid Recommendation Methodsmentioning

confidence: 99%

A Bayesian Inference Based Hybrid Recommender System

2020

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The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations. Recommender systems have been developed to tackle the item targeting problem. They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences. Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches. Collaborative filtering-based recommender approaches are the most widely adopted methods. They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate. In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. Our method estimates the probability with which a user would rate an item. It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of modelbased methods. Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality.

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Section: Hybrid Recommendation Methodsmentioning

confidence: 99%

A Bayesian Inference Based Hybrid Recommender System

2020

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“…Item list continuation aims to continue the user-curated list by item sequence generation while maintaining its consistency and coherence, which has received limited study. One line of research focuses on automatic playlist continuation [2], which has gained much attention [7,28,31]. However, these methods are often tailored for music playlists.…”

Section: Related Workmentioning

confidence: 99%

FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation

Liu

Zhu

et al. 2023

Proceedings of the ACM Web Conference 2023

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User-curated item lists, such as video-based playlists on Youtube and book-based lists on Goodreads, have become prevalent for content sharing on online platforms. Item list continuation is proposed to model the overall trend of a list and predict subsequent items. Recently, Transformer-based models have shown promise in comprehending contextual information and capturing item relationships in a list. However, deploying them in real-time industrial applications is challenging, mainly because the autoregressive generation mechanism used in them is time-consuming. In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. First, we use a non-autoregressive generation mechanism to decode next 𝐾 items simultaneously instead of one by one in existing models. Then, we design a two-stage classifier to replace the vanilla classifier used in current transformer-based models to further reduce the decoding time. Moreover, to improve the quality of non-autoregressive generation, we employ a curriculum learning strategy to optimize training. Experimental results on four real-world item list continuation datasets including Zhihu, Spotify, AotM, and Goodreads show that our FANS model can significantly improve inference efficiency (up to 8.7x) while achieving competitive or better generation quality for item list continuation compared with the state-of-the-art autoregressive models. We also validate the efficiency of FANS in an industrial setting. Our source code and data will be available at MindSpore/models

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“…Moreover, a Hybrid Recommender System for Improving Automatic Playlist Continuation was studied and developed by Gatzioura et al (2021). The said approach has proven an excellent help for Music listeners to find similar items and suggest other playlists related to the recent activities.…”

Section: Literature Reviewmentioning

confidence: 99%

A Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA)

Lecaros¹,

Khan²

2022

IJCSR

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Purpose – Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and user experience design of a modern system. Method – Two machine learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering (CBF); (2) Classification with Matrix Factorization- Singular Value Decomposition (SVD) applied with Collaborative filtering (CF) and mean (normalization) for prediction accuracy of the CF. Data sampling in academic Research and Development (R&D) of Philippine Council for Agriculture, Aquatic, and Natural Resources Research and Development (PCAARRD) e-Library and Project SARAI publications plus simulated data used as training sets to generate a recommendation of items that uses the three RS filtering (CF, CBF, and personalized version of item recommendations). Series of Testing and TAM performed and discussed. Results – Findings allow users to engage in online information and quickly evaluate retrieved items produced by the application. Compatibility-testing (CoT) shows the application is compatible with all major browsers and mobile-friendly. Performance-testing (PT) recommended v-parameter specs and TAM evaluations results indicate strongly associated with overall positive feedback, thoroughly enough to address the information-overload problem as the core of the paper. Conclusion – A modular architecture presented addressing the information overload, primarily focused on RSs with the personalization and design of modern systems. Developers utilized Two ML algorithms and prototyped a simplified version of the architecture. Series of testing (CoT and PT) and evaluations with TAM were performed and discussed. Project ATHENA added a UX feature design of a modern system. Recommendations – High-end hardware specs of v-Parameter are recommended, at least 8-cores of vCPU with 16GiB of memory and sufficient driver-type size to run the model and End-to-end jobs execution for continuously maintaining the application. Research Implications – Future Developers must use/integrate a large dataset. Other ML approaches can expand Hybrid-RS better. Implicit data and additional filtering methods may enhance the application in the future.

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A Hybrid Recommender System for Improving Automatic Playlist Continuation

Cited by 17 publications

References 32 publications

A Bayesian Inference Based Hybrid Recommender System

A Bayesian Inference Based Hybrid Recommender System

FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation

A Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA)

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