# Attentive Item2Vec: Neural Attentive User Representations

arXiv: https://arxiv.org/abs/2002.06205 · HTML: https://mlsanity.com/paper/2002.06205/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps](https://mlsanity.com/paper/2204.11073/) (2022)
- [Deep Integrated Explanations](https://mlsanity.com/paper/2310.15368/) (2023)
- [Interpreting BERT-based Text Similarity via Activation and Saliency Maps](https://mlsanity.com/paper/2208.06612/) (2022)
- [Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates](https://mlsanity.com/paper/2112.07615/) (2021)
- [Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models](https://mlsanity.com/paper/2310.16584/) (2023)
- [MetricBERT: Text Representation Learning via Self-Supervised Triplet Training](https://mlsanity.com/paper/2208.06610/) (2022)
- [Representation Learning via Variational Bayesian Networks](https://mlsanity.com/paper/2306.16326/) (2023)
- [Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering](https://mlsanity.com/paper/2310.14215/) (2023)
- [Large-scale User Game Lifecycle Representation Learning](https://mlsanity.com/paper/2510.15412/) (2025)

## Abstract (arXiv)

Factorization methods for recommender systems tend to represent users as a single latent vector. However, user behavior and interests may change in the context of the recommendations that are presented to the user. For example, in the case of movie recommendations, it is usually true that earlier user data is less informative than more recent data. However, it is possible that a certain early movie may become suddenly more relevant in the presence of a popular sequel movie. This is just a single example of a variety of possible dynamically altering user interests in the presence of a potential new recommendation. In this work, we present Attentive Item2vec (AI2V) - a novel attentive version of Item2vec (I2V). AI2V employs a context-target attention mechanism in order to learn and capture different characteristics of user historical behavior (context) with respect to a potential recommended item (target). The attentive context-target mechanism enables a final neural attentive user representation. We demonstrate the effectiveness of AI2V on several datasets, where it is shown to outperform other baselines.