Mostra: Balancing a number of aims for music advice


April 27, 2022 Published by Emanuele Bugliarello and Mounia Lalmas

Recommendation engines assist most trendy digital platforms, permitting customers to navigate huge databases of merchandise in Amazon, houses in AirBnB, movies in YouTube and music in Spotify. However, content material in all these platforms is offered by creators, resembling hosts in AirBnB and artists in Spotify, who play an important position in shaping the consumer expertise. As such, it’s essential to assist their publicity wants to make sure long-term engagement.

  1. In an upcoming paper at TheWebConf 2022, we research the multi-objective drawback of learn how to greatest steadiness consumer, creator and platform wants for the long-term well being and sustainability of Spotify. In explicit:
  2. We present that there’s a huge heterogeneity of multi-objective music streaming periods.
  3. We discover proof from historic knowledge that consumer satisfaction varies primarily based on the kind and quantity of tracks that profit a number of stakeholders.
  4. We suggest Mostra: a brand new framework primarily based on state-of-the-art Transformer applied sciences that we equip with counterfactual reasoning to permit system designers to dynamically and flexibly management the trade-offs throughout the completely different aims primarily based on the ever-evolving strategic wants.

Objectives in music platforms

The final purpose behind our work is to grasp and leverage the trade-off throughout completely different objectives for music advice, the place the duty consists of ordering a set of songs to fulfill a number of aims. For this, we contemplate knowledge from tens of millions of radio periods. To characterise the completely different stakeholders, we contemplate the next metrics for every streamed tune:

  • SAT: Whether a consumer fully listened to the tune. if a consumer did take heed to it, they’re more likely to prefer it! ?
  • Discovery: Whether a consumer has by no means listened to that tune nor to any songs from the corresponding artist. This is what permits customers to expertise new music! ?
  • Exposure: Whether the tune belongs to an rising artist. This makes Spotify a sustainable platform to assist all kinds of creators! ?
  • Boosting: Whether the tune belongs to a gaggle that the platform is fascinated with boosting. It might be primarily based on latest occasions, to align with sure traits, or to rejoice and honour a gaggle of artists. ?

To higher perceive the interaction between the creator-centric aims and customers’ short-term satisfaction, and the way frequent a few of these aims are inside periods, we contemplate a random pattern of 100M listening periods. As proven within the determine above, customers get pleasure from songs being boosted or from emergent artists! However, if a given session comprises too many discoveries, customers are likely to skip most of them, and so explicit care have to be taken in recommending new content material to make sure maximal consumer satisfaction.

Interestingly, the plot above exhibits that there’s a excessive variety within the form of tracks streamed by our listeners. What this implies is that (i) there may be extra extreme competitors throughout aims in sure periods than others, and (ii) a dynamic, multi-objective advice engine is required to adapt suggestions to every distinctive situation.

Welcome to the Mostra

How can we construct a recommender system that leverages all these objectives in an efficient manner? This is an open query! In this paper, we suggest a primary step on this route. Specifically, our focus is on constructing a dynamic system that may simply be tuned by system engineers on-the-fly to focus on particular aims.

Mostra (Multi-objective Set Transformer), proven beneath, is our end-to-end neural community that mixes state-of-the-art Transformer encoders with a novel beam search algorithm that selects the subsequent tune by reasoning about consumer satisfaction and the creators’ aims.

Mostra works as follows:

  1. First, we encode every tune within the pool utilizing an encoder educated to maximise consumer satisfaction. This is what music advice techniques are normally optimised for.
  2. Second, given an encoded illustration for every tune within the pool, we first tag every tune that has a creator goal. 
  3. Then, as a substitute of selecting the tune that may maximise the coaching goal, we use a counterfactual step the place tagged songs whose predicted rating is inside a small distinction, epsilon, from the highest one to be re-scored primarily based on their creator-centric aims.
    In explicit, we use a submodular scoring perform that maximises the variety of aims coated up to now.
  4. Finally, if a tune that’s tagged with creator-centric aims receives the next rating than one with none of these aims, that tune is given to the listener as the subsequent monitor of their stream.

The energy of Mostra is in permitting just-in-time, on-the-fly modifications to the balancing of a number of aims inside the set. In reality, one wants to not re-encode a given monitor however quite solely change both (i) the utmost distinction of predicted scores allowed for re-ranking, (ii) the aims that must be thought-about at a given time, and/or (iii) their significance.


We examine Mostra with completely different threshold epsilon in opposition to plenty of recommender approaches, together with each relevance-based strategies and state-of-the-art neural fashions. As proven within the desk beneath, Mostra can enhance upon them throughout all aims, particularly on the creator-centric ones.

We additional research the behaviour of Mostra throughout varied axes. In explicit, we present that (i) Mostra can leverage multi-objective music units with solely minor losses for Discovery tracks (which we noticed earlier are negatively correlated with SAT); (ii) Mostra not solely recommends extra creator-centric songs however these which are literally preferred (i.e. totally listened to) by the listeners. 

In addition, the decoding algorithm of Mostra might be tailored to any accessible recommender system to make it a dynamic, just-in-time multi-objective engine! We present that our DNN baseline can profit from it simply as a lot as our Transformer-based Mostra mannequin. 


We studied the issue of multi-objective advice, a key problem in typical multi-stakeholder platforms resembling Spotify. We first carried out a complete evaluation of the completely different aims and their interaction. Then, we proposed Mostra, a neural community that recommends songs primarily based on varied aims that may simply be managed to fulfill dynamic strategic wants via counterfactual decoding. Mostra achieves aggressive efficiency on short-term consumer satisfaction while largely enhancing on different creator-centric aims.

Check out our paper for a lot of extra particulars:
Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing. 
Emanuele Bugliarello, Rishabh Mehrotra, James Kirk, and Mounia Lalmas
The Web Conference (WWW) 2022.


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