Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Nazari, Zahra; Charbuillet, Christophe; Pages, Johan; Martin, Laurent; Charrier, Denis; Vecchione, Briana; Carterette, Ben

doi:10.1145/3397271.3401101

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…Different from the above works, our work aims to improve CTR prediction performance on cold users with very few historical behaviors. To address the problem, some works exploited external user information such as social networks [24,36] and cross-domain user behaviors [3,8,9,17,23,38,49]. For example, Hu et al [24] proposed to build graph neural networks based on users' social relations and enhance cold user representations by propagating information from neighboring users.…”

Section: Related Workmentioning

confidence: 99%

RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction

Shen

Zhao

Cheng

et al. 2023

ACM Trans. Inf. Syst.

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Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt optimization-based meta-learning. However, existing methods suffer from information loss or inefficient optimization process, and they fail to explicitly model global user preference knowledge which is crucial to complement the sparse and insufficient preference information of cold users. In this paper, we propose a novel and efficient approach named RESUS, which decouples the learning of global preference knowledge contributed by collective users from the learning of residual preferences for individual users. Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users. Meanwhile, we develop two efficient algorithms based on the nearest neighbor and ridge regression predictors, which infer residual user preferences via learning quickly from a few user-specific interactions. Extensive experiments on three public datasets demonstrate that our RESUS approach is efficient and effective in improving CTR prediction accuracy on cold users, compared with various state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction

Shen

Zhao

Cheng

et al. 2023

ACM Trans. Inf. Syst.

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“…Learning deep audio features HLDBN [130] [131], [132] Video Attention networks to learn video representations from multiple image representations ACF [19], JIFR [133], AGCN [134] GNNs to learn video representation AGCN [134]…”

Section: Audiomentioning

confidence: 99%

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Wu,

He,

Wang

et al. 2021

Preprint

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Influenced by the stunning success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models, aiming to summarize the field to facilitate future progress. Distinct from existing surveys that categorize existing methods based on the taxonomy of deep learning techniques, we instead summarize the field from the perspective of recommendation modeling, which could be more instructive to researchers and practitioners working on recommender systems. Specifically, we divide the work into three types based on the data they used for recommendation modeling: 1) collaborative filtering models, which leverage the key source of user-item interaction data; 2) content enriched models, which additionally utilize the side information associated with users and items, like user profile and item knowledge graph; and 3) context enriched models, which account for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative works for each type, we finally discuss some promising directions in this field, including benchmarking recommender systems, graph reasoning based recommendation models, and explainable and fair recommendations for social good.

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“…Since it is often music streaming platforms that extend their catalogs to include podcasts, a natural choice to address the cold-start problem (in this case, missing initial user-podcast interactions by existing users), is to adopt a cross-domain recommendation approach. In particular, using music preferences in a cross-domain fashion to address cold-start in podcast recommendation has been shown to be successful [56]. On the other hand, domains such as movies or books have not yet been investigated for cross-domain podcast recommendation.…”

Section: Podcast Recommendationmentioning

confidence: 99%

Current Challenges and Future Directions in Podcast Information Access

Jones¹,

Zamani

Schedl

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

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Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators. The great strides in search and recommendation in research and industry have yet to see impact in the podcast space, where recommendations are still largely driven by word of mouth. In this perspective paper, we highlight the many differences between podcasts and other media, and discuss our perspective on challenges and future research directions in the domain of podcast information access.

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Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Cited by 17 publications

References 26 publications

RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction

RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Current Challenges and Future Directions in Podcast Information Access

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