Sequential choice in ordered bundles

TL;DR

This paper investigates how individuals decide the next item to consume in ordered bundles like playlists, evaluating various predictive models and finding that a custom Transformer model most accurately predicts consumption patterns and demand.

Contribution

It introduces a Transformer-based predictive model for sequential consumption in ordered bundles, demonstrating its effectiveness over other models in predicting individual choices and demand.

Findings

Transformer model outperforms other models in prediction accuracy

Consumption of next items is based on equal weighting of all previous choices

Transformer can personalize promotions and forecast demand effectively

Abstract

Experience goods such as sporting and artistic events, songs, videos, news stories, podcasts, and television series, are often packaged and consumed in bundles. Many such bundles are ordered in the sense that the individual items are consumed sequentially, one at a time. We examine if an individual's decision to consume the next item in an ordered bundle can be predicted based on his/her consumption pattern for the preceding items. We evaluate several predictive models, including two custom Transformers using decoder-only and encoder-decoder architectures, fine-tuned GPT-3, a custom LSTM model, a reinforcement learning model, two Markov models, and a zero-order model. Using data from Spotify, we find that the custom Transformer with a decoder-only architecture provides the most accurate predictions, both for individual choices and aggregate demand. This model captures a general form of state dependence. Analysis of Transformer attention weights suggests that the consumption of the next item in a bundle is based on approximately equal weighting of all preceding choices. Our results indicate that the Transformer can assist in queuing the next item that an individual is likely to consume from an ordered bundle, predicting the demand for individual items, and personalizing promotions to increase demand.