# Next Period Recommendation Reality Check

arXiv: https://arxiv.org/abs/2110.05589 · HTML: https://mlsanity.com/paper/2110.05589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [Sequence-Aware Recommender Systems](https://mlsanity.com/paper/1802.08452/) (2018)
- [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://mlsanity.com/paper/1812.02646/) (2018)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [How to Retrain Recommender System? A Sequential Meta-Learning Method](https://mlsanity.com/paper/2005.13258/) (2020)
- [Modeling Personalized Item Frequency Information for Next-basket Recommendation](https://mlsanity.com/paper/2006.00556/) (2020)
- [A Re-visit of the Popularity Baseline in Recommender Systems](https://mlsanity.com/paper/2005.13829/) (2020)
- [It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]](https://mlsanity.com/paper/1708.08447/) (2017)
- [Triple2Vec: Learning Triple Embeddings from Knowledge Graphs](https://mlsanity.com/paper/1905.11691/) (2019)

## Abstract (arXiv)

Over the past decade, tremendous progress has been made in Recommender Systems (RecSys) for well-known tasks such as next-item and next-basket prediction. On the other hand, the recently proposed next-period recommendation (NPR) task is not covered as much. Current works about NPR are mostly based around distinct problem formulations, methods, and proprietary datasets, making solutions difficult to reproduce. In this article, we aim to fill the gap in RecSys methods evaluation on the NPR task using publicly available datasets and (1) introduce the TTRS, a large-scale financial transactions dataset suitable for RecSys methods evaluation; (2) benchmark popular RecSys approaches on several datasets for the NPR task. When performing our analysis, we found a strong repetitive consumption pattern in several real-world datasets. With this setup, our results suggest that the repetitive nature of data is still hard to generalize for the evaluated RecSys methods, and novel item prediction performance is still questionable.