Accounting for User Aspirations in Podcast Recommendations

Accounting for User Aspirations in Podcast Recommendations



Podcasting as a medium is growing exponentially, with hundreds of thousands of shows available in genres from comedy to news reporting to true crime storytelling to health and wellness to education and self-directed learning. With such a great variety of content, it is natural that listeners would approach a podcast catalog with some aspirational goals (such as learning a language or eating healthier), while also desiring simpler pleasures such as entertainment or having a feeling of some connection via listening . Recommender systems help listeners find content to listen to, but how can they account for the different goals listeners might have?
Recommender systems are typically trained to some target engagement signal such as clicks, streams, likes, or a weighted combination. In this work, we dive deeper into this target choice by focusing on podcast recommendations at Spotify. There are now more than one million active podcast shows consisting of 64 million episodes. Podcast shows are distributed by RSS feeds; people subscribe to a show to automatically receive each new episode. But it is also common to “dip in” and just listen to single podcast episodes. Thus podcasts offer two strong engagement signals: show subscriptions and episode plays. Certain types of subscriptions may reflect aspirational goals listeners have; in particular, subscribing to a language-learning podcast, a news podcast, or a health and wellness podcast suggests that the user has aspirations that they hope to achieve. But episode plays do not necessarily follow from show subscriptions; the recommender system, by filtering what users see first, mediates plays, and how it does so depends greatly on the choice of target engagement signal to which to train.
In this work we take advantage of this distinguishing property of the podcast domain to tackle the problem of optimizing recommendations in the presence of multiple implicit engagement signals. We explore this problem by answering three main research questions:

What is the impact on recommendations and consumption when training for “plays” versus “subscriptions”?

What are the strongest factors that predict how listeners engage with shows?

How could we use calibration to make an informed decision and leverage both signals in recommendations?

Effect of optimization signal on the top-n recommended items and user consumption

Our goal here is to highlight the differences observed when optimizing recommendation algorithms based on different engagement signals. We use a recommendation algorithm based on deep neural networks that has shown promising results in similar recommendation applications. The framework casts the recommendations problem as an extreme multi-class classification task modeled by a multilayer perceptron. This provides flexibility in handling heterogeneous feature sets, and the approach is widely used across recommender systems that operate in large scales.
We train this model twice:

Subscribe Model: User-show pairs are assigned a positive label if the user has subscribed to a show. Users can subscribe even before listening to any of its episodes.

Play Model: User-show pairs are assigned a positive label if the user streams at least one episode of the show.

We summarize the recommendations provided by each model by using the category of a show and aggregating top shows that are recommended across users. The distribution of recommended items in each model shows large differences between categories:


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