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Job Recommender Systems: A Review [article]

Corné de Ruijt, Sandjai Bhulai
2021 arXiv   pre-print
This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011-2021).  ...  Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations.  ...  The latter two both used a dataset from the German job board Xing [121] .  ... 
arXiv:2111.13576v1 fatcat:hlm2dowihjd33p55jgexueefbq

A scalable, high-performance Algorithm for hybrid job recommendations

Toon De Pessemier, Kris Vanhecke, Luc Martens
2016 Proceedings of the Recommender Systems Challenge on - RecSys Challenge '16  
The contentbased algorithm matches features of candidate recommendations and job postings of historical interactions.  ...  Recommender systems can be used as a tool to assist people in finding a job.  ...  Job recommendation is also the topic of the RecSys Challenge 2016 [2] , which is co-organized by XING. XING is a social network for business.  ... 
doi:10.1145/2987538.2987539 dblp:conf/recsys/PessemierVM16 fatcat:pdhnc5lhe5hp7nit2b3h7tievu

Using autoencoders for session-based job recommendations

Emanuel Lacic, Markus Reiter-Haas, Dominik Kowald, Manoj Reddy Dareddy, Junghoo Cho, Elisabeth Lex
2020 User modeling and user-adapted interaction  
We evaluate our approach on three datasets, (1) a proprietary dataset we gathered from the Austrian student job portal Studo Jobs, (2) a dataset released by XING after the RecSys 2017 Challenge and (3)  ...  Our results show that autoencoders provide relevant job recommendations as well as maintain a high coverage and, at the same time, can outperform state-of-the-art session-based recommendation techniques  ...  This work is supported by the Know-Center, the Institute of Interactive Systems and Data Science (ISDS) of Graz University of Technology and Moshbit.  ... 
doi:10.1007/s11257-020-09269-1 fatcat:6lqphfv425b4rjzvgzcqit3tpq

Temporal learning and sequence modeling for a job recommender system

Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, Prem Natarajan
2016 Proceedings of the Recommender Systems Challenge on - RecSys Challenge '16  
We present our solution to the job recommendation task for RecSys Challenge 2016.  ...  The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations.  ...  Given the profile of users, job postings (items), and their interaction history on Xing, the goal is to predict a ranked list of items of interest to a user.  ... 
doi:10.1145/2987538.2987540 dblp:conf/recsys/LiuSKZN16 fatcat:wsmeotl5qvbhnixxiog5jexwzu

Making Job Postings More Equitable: Evidence Based Recommendations from an Analysis of Data Professionals Job Postings Between 2013-2018

Joanna Thielen, Amy Neeser
2020 Evidence Based Library and Information Practice  
based recommendations regarding how the profession can enact meaningful and lasting change in the areas of DEI&A.  ...  Results - Over one-third of postings (n = 63, 35%) did not use the word "librarian" in the job title.  ...  Acknowledgements The authors thank Kristin Briney for reviewing the codebook, as well as Marie Kennedy, Abigail Goben, and Tina Griffin for reviewing a draft of this article and providing valuable feedback  ... 
doi:10.18438/eblip29674 fatcat:leeux4hktjdtlk72oagvt2b6le

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi
2017 Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17  
Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings.  ...  Results on two industry datasets show large improvements over the session-only RNNs.  ...  One possible explanation could be that the consumption of multimedia content (videos in our case) is a strongly session-based scenario, much stronger than the job search scenario represented in XING.  ... 
doi:10.1145/3109859.3109896 dblp:conf/recsys/QuadranaKHC17 fatcat:zlizm2zazjgilgojsfunlb7rwm

Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation [article]

Shu Wu, Mengqi Zhang, Xin Jiang, Ke Xu, Liang Wang
2020 arXiv   pre-print
The problem of session-aware recommendation aims to predict users' next click based on their current session and historical sessions.  ...  Extensive experiments conducted on two real-world data sets show that A-PGNN evidently outperforms the state-of-the-art personalized session-aware recommendation methods.  ...  The Xing data set contains interactions on job postings for 770,000 users over an 80-day period. In these data, user behaviors include click, bookmark, reply, and delete.  ... 
arXiv:1910.08887v3 fatcat:jkkiqvthtbghlpaqlq7556crte

TorchRec: a PyTorch Domain Library for Recommendation Systems

Dmytro Ivchenko, Dennis Van Der Staay, Colin Taylor, Xing Liu, Will Feng, Rahul Kindi, Anirudh Sudarshan, Shahin Sefati
2022 Sixteenth ACM Conference on Recommender Systems  
In this talk we cover the building blocks of the TorchRec library including modeling primitives such as embedding bags and jagged tensors, optimized recommender system kernels powered by FBGEMM, a flexible  ...  The neural network-based recommender systems differ from deep learning models in other domains in using high-cardinality categorical sparse features that require large embedding tables to be trained.  ...  He is a part of PyTorch team focusing on recommender systems. Prior to Meta Dmytro worked at LinkedIn where he wrote personalized search engine used to search for people, jobs and other site content.  ... 
doi:10.1145/3523227.3547387 fatcat:uhjcevafvbdihjpnwyhkyfadeq

A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes [article]

Kuan Liu and Xing Shi and Prem Natarajan
2018 arXiv   pre-print
The new approach show significant improvements over the state-of-the-art models.  ...  Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems.  ...  The information might be used in cases where a new business is recommended to a Yelp customer. Some other examples (including those from XING) are listed in Table 10 .  ... 
arXiv:1805.11008v1 fatcat:peq4hcvodjbunbgce2qdz5boam

Recommender Systems Fairness Evaluation via Generalized Cross Entropy [article]

Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, Tommaso Di Noia
2019 arXiv   pre-print
We present a probabilistic framework based on generalized cross entropy to evaluate fairness of recommender systems under this perspective, where we show that the proposed framework is flexible and explanatory  ...  Regardless, the concept has been commonly interpreted as some form of equality -- i.e., the degree to which the system is meeting the information needs of all its users in an equal sense.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.  ... 
arXiv:1908.06708v1 fatcat:hz7bd6ynl5b4hb2jouclojx3di

Recommender systems based on hybrid models [chapter]

Alberto Rivas, Pablo Chamoso, Alfonso González-Briones
2020 The role of Artificial Intelligence and distributed computing in IoT applications  
Finally, the last step on the CBR is to propose recommendations to the final user, whose job is to validate or reject the proposal feeding the cases database.  ...  The first case is a work related to social networks in which individual job recommendations are given based on the preferences and the profile of a particular user on social networks.  ...  We often see them as simple communication and content-sharing 2 Job offer recommender system The research presented in this case study focuses on a relationship recommendation system for a business and  ... 
doi:10.14201/0aq0287135148 fatcat:qc5smv6xqjgedirfvqvo2ecdqe

Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation [article]

Younghun Song, Jae-Gil Lee
2018 arXiv   pre-print
Therefore, in this paper, we explore the utility of explicit user-side context modeling for RNN session models.  ...  The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches.  ...  Rich user-side categorical contexts such as job roles and career levels are included in the XING dataset.  ... 
arXiv:1805.02983v1 fatcat:cnfoz2rl4vhtzdam3hzqjuw55a

Content-based Neighbor Models for Cold Start in Recommender Systems

Maksims Volkovs, Guang Wei Yu, Tomi Poutanen
2017 Proceedings of the Recommender Systems Challenge 2017 on ZZZ - RecSys Challenge '17  
The challenge organizer XING released a large scaled data collection of user-job interactions from their career oriented social network.  ...  Top models were then A/B tested in the online phase where new target users and items were released daily and recommendations were pushed into XING's live production system.  ...  All target items were cold start simulating the production scenario for XING where newly added jobs need to be recommended to relevant users.  ... 
doi:10.1145/3124791.3124792 fatcat:kbr3n2ojhrgvdngz7uhlloakl4

Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)

Toine Bogers, David Graus, Mesut Kaya, Francisco Gutiérrez, Sepideh Mesbah, Chris Johnson
2022 Sixteenth ACM Conference on Recommender Systems  
The use of AI applications in the recruitment process, such as recommender systems, is considered high-risk by the European Commission [23], as automation here can directly impact the (working) lives of  ...  16] , to broader tasks such as recommendations for upskilling [21] .  ...  In addition to this research, the RecSys Challenges of 2016 [1] and 2017 [2] both focused on the task of job recommendation, with Xing, a social network for businesses mainly operating in German-speaking  ... 
doi:10.1145/3523227.3547414 fatcat:nqeyv3diwnao5m3o27q4woqjne

Optimally balancing receiver and recommended users' importance in reciprocal recommender systems

Akiva Kleinerman, Ariel Rosenfeld, Francesco Ricci, Sarit Kraus
2018 Proceedings of the 12th ACM Conference on Recommender Systems - RecSys '18  
Online platforms which assist people in finding a suitable partner or match, such as online dating and job recruiting environments, have become increasingly popular in the last decade.  ...  For each user receiving a recommendation, our method finds the optimal balance of two criteria: a) the likelihood of the user accepting the recommendation; and b) the likelihood of the recommended user  ...  The participant teams were given a large dataset from XING 1 , a career-oriented social network, that consisted of anonymized user profiles, job postings, and interactions between them.  ... 
doi:10.1145/3240323.3240349 dblp:conf/recsys/KleinermanR0K18 fatcat:hadpzsntwjeyrby7wsgqykagna
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