Episodes Discovery Recommendation with Multi-Source Augmentations

• URL: http://arxiv.org/abs/2301.01737v1
• Date: Wed, 4 Jan 2023 18:10:26 GMT
• Title: Episodes Discovery Recommendation with Multi-Source Augmentations
• Authors: Ziwei Fan, Alice Wang, and Zahra Nazari
• Abstract summary: We build upon the classical Two-Tower model and introduce the novel Multi-Source Augmentations (MSACL) to enhance episode embedding learning. Experiments on a real-world podcast recommendation dataset from a large audio streaming platform demonstrate the effectiveness of the proposed framework.
• Score: 3.0054316799543837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems (RS) commonly retrieve potential candidate items for users from a massive number of items by modeling user interests based on historical interactions. However, historical interaction data is highly sparse, and most items are long-tail items, which limits the representation learning for item discovery. This problem is further augmented by the discovery of novel or cold-start items. For example, after a user displays interest in bitcoin financial investment shows in the podcast space, a recommender system may want to suggest, e.g., a newly released blockchain episode from a more technical show. Episode correlations help the discovery, especially when interaction data of episodes is limited. Accordingly, we build upon the classical Two-Tower model and introduce the novel Multi-Source Augmentations using a Contrastive Learning framework (MSACL) to enhance episode embedding learning by incorporating positive episodes from numerous correlated semantics. Extensive experiments on a real-world podcast recommendation dataset from a large audio streaming platform demonstrate the effectiveness of the proposed framework for user podcast exploration and cold-start episode recommendation.

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