# Improving Next-Application Prediction with Deep Personalized-Attention Neural Network

arXiv: https://arxiv.org/abs/2111.11296 · HTML: https://mlsanity.com/paper/2111.11296/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [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)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach](https://mlsanity.com/paper/1812.08947/) (2018)
- [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018)
- [Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning](https://mlsanity.com/paper/2009.07429/) (2020)
- [News Session-Based Recommendations using Deep Neural Networks](https://mlsanity.com/paper/1808.00076/) (2018)
- [Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks](https://mlsanity.com/paper/1904.10367/) (2019)
- [Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations](https://mlsanity.com/paper/1905.01997/) (2019)

## Built on by (most cited first, 1 of 1)

- [Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning](https://mlsanity.com/paper/2206.02782/) (2022)

## Abstract (arXiv)

Recently, due to the ubiquity and supremacy of E-recruitment platforms, job recommender systems have been largely studied. In this paper, we tackle the next job application problem, which has many practical applications. In particular, we propose to leverage next-item recommendation approaches to consider better the job seeker's career preference to discover the next relevant job postings (referred to jobs for short) they might apply for. Our proposed model, named Personalized-Attention Next-Application Prediction (PANAP), is composed of three modules. The first module learns job representations from textual content and metadata attributes in an unsupervised way. The second module learns job seeker representations. It includes a personalized-attention mechanism that can adapt the importance of each job in the learned career preference representation to the specific job seeker's profile. The attention mechanism also brings some interpretability to learned representations. Then, the third module models the Next-Application Prediction task as a top-K search process based on the similarity of representations. In addition, the geographic location is an essential factor that affects the preferences of job seekers in the recruitment domain. Therefore, we explore the influence of geographic location on the model performance from the perspective of negative sampling strategies. Experiments on the public CareerBuilder12 dataset show the interest in our approach.