透過圖神經網絡在 Spotify 提供個性化有聲書推薦

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

March 8, 2024

作者: Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou

cs.AI

摘要

在不斷演進的數位音訊領域中，以其音樂和談話內容而聞名的 Spotify 近期向廣大用戶推出有聲書。儘管前景看好，這一舉措為個性化推薦帶來了重大挑戰。與音樂和播客不同，最初需要付費購買的有聲書無法在購買前輕易瀏覽，因此對推薦的相關性提出了更高的要求。此外，將新的內容類型引入現有平台面臨極端的數據稀疏性，因為大多數用戶對這種新內容類型不熟悉。最後，向數百萬用戶推薦內容需要模型快速反應並具有可擴展性。為應對這些挑戰，我們利用播客和音樂用戶偏好，引入了一個可擴展的推薦系統 2T-HGNN，其中包括異構圖神經網絡（HGNNs）和雙塔（2T）模型。這種新穎方法揭示了物品之間微妙的關係，同時確保低延遲和複雜性。我們將用戶從 HGNN 圖中分離出來，並提出了一個創新的多連接鄰居抽樣器。這些選擇，再加上 2T 元件，顯著降低了 HGNN 模型的複雜性。通過涉及數百萬用戶的實證評估，我們發現個性化推薦的質量顯著提高，導致新有聲書的啟動率增加了 +46%，流量率提高了 +23%。有趣的是，我們的模型影響不僅限於有聲書，還惠及播客等既有產品。

English

In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this move presents significant challenges for personalized recommendations. Unlike music and podcasts, audiobooks, initially available for a fee, cannot be easily skimmed before purchase, posing higher stakes for the relevance of recommendations. Furthermore, introducing a new content type into an existing platform confronts extreme data sparsity, as most users are unfamiliar with this new content type. Lastly, recommending content to millions of users requires the model to react fast and be scalable. To address these challenges, we leverage podcast and music user preferences and introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach uncovers nuanced item relationships while ensuring low latency and complexity. We decouple users from the HGNN graph and propose an innovative multi-link neighbor sampler. These choices, together with the 2T component, significantly reduce the complexity of the HGNN model. Empirical evaluations involving millions of users show significant improvement in the quality of personalized recommendations, resulting in a +46% increase in new audiobooks start rate and a +23% boost in streaming rates. Intriguingly, our model's impact extends beyond audiobooks, benefiting established products like podcasts.