# Future-Aware Diverse Trends Framework for Recommendation

arXiv: https://arxiv.org/abs/2011.00422 · HTML: https://mlsanity.com/paper/2011.00422/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Why Do We Click: Visual Impression-aware News Recommendation](https://mlsanity.com/paper/2109.12651/) (2021)
- [Intelligent Request Strategy Design in Recommender System](https://mlsanity.com/paper/2206.12296/) (2022)
- [Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization](https://mlsanity.com/paper/2502.13180/) (2025)
- [Modeling High-order Interactions across Multi-interests for Micro-video Recommendation](https://mlsanity.com/paper/2104.00305/) (2021)
- [Memory-Inspired Temporal Prompt Interaction for Text-Image Classification](https://mlsanity.com/paper/2401.14856/) (2024)
- [EPIC: Efficient Prompt Interaction for Text-Image Classification](https://mlsanity.com/paper/2507.07415/) (2025)
- [Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models](https://mlsanity.com/paper/2508.04032/) (2025)

## Abstract (arXiv)

In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between historically interacted items for capturing users' historical preferences. However, since users' preferences are by nature time-evolving and diversified, solely modeling the historical preference (without being aware of the time-evolving trends of preferences) can be inferior for recommending complementary or fresh items and thus hurt the effectiveness of recommender systems. In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. By future-aware, for each inspected user, we construct the future sequences from other similar users, which comprise of behaviors that happen after the last behavior of the inspected user, based on a proposed neighbor behavior extractor. By diverse trends, supposing the future preferences can be diversified, we propose the diverse trends extractor and the time-aware mechanism to represent the possible trends of preferences for a given user with multiple vectors. We leverage both the representations of historical preference and possible future trends to obtain the final recommendation. The quantitative and qualitative results from relatively extensive experiments on real-world datasets demonstrate the proposed framework not only outperforms the state-of-the-art sequential recommendation methods across various metrics, but also makes complementary and fresh recommendations.