# Using Temporal Data for Making Recommendations

arXiv: https://arxiv.org/abs/1301.2320 · HTML: https://mlsanity.com/paper/1301.2320/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2013.

## Built on by (most cited first, 20 of 20)

- [DiffuRec: A Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2304.00686/) (2023)
- [Decoupled Side Information Fusion for Sequential Recommendation](https://mlsanity.com/paper/2204.11046/) (2022)
- [A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models](https://mlsanity.com/paper/2107.13045/) (2021)
- [Effective and Efficient Training for Sequential Recommendation using Recency Sampling](https://mlsanity.com/paper/2207.02643/) (2022)
- [Improving Sequential Recommendation Consistency with Self-Supervised Imitation](https://mlsanity.com/paper/2106.14031/) (2021)
- [player2vec: A Language Modeling Approach to Understand Player Behavior in Games](https://mlsanity.com/paper/2404.04234/) (2024)
- [Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decoupling](https://mlsanity.com/paper/2110.05409/) (2021)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [G3SR: Global Graph Guided Session-based Recommendation](https://mlsanity.com/paper/2203.06467/) (2022)
- [Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems](https://mlsanity.com/paper/2210.12940/) (2022)
- [Graph Spring Network and Informative Anchor Selection for Session-based Recommendation](https://mlsanity.com/paper/2202.09502/) (2022)
- [BiVRec: Bidirectional View-based Multimodal Sequential Recommendation](https://mlsanity.com/paper/2402.17334/) (2024)
- [Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study](https://mlsanity.com/paper/2410.18605/) (2024)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking](https://mlsanity.com/paper/2210.10547/) (2022)
- [COUPA: An Industrial Recommender System for Online to Offline Service Platforms](https://mlsanity.com/paper/2304.12549/) (2023)
- [Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2311.02938/) (2023)
- [BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model](https://mlsanity.com/paper/2512.13848/) (2025)
- [Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook](https://mlsanity.com/paper/2606.29762/) (2026)
- [Residual Dominance as a Structural Account of Last-Item Reliance in Causal Self-Attention Recommenders](https://mlsanity.com/paper/2608.14021/) (2026)

## Abstract (arXiv)

We treat collaborative filtering as a univariate time series estimation problem: given a user's previous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estimation tools, and examine the results of using these approaches on several real-world data sets. The improvements in predictive accuracy we realize recommend the use of other predictive algorithms that exploit the temporal order of data.