Tree-Based Contextual Learning for Online Job or Candidate Recommendation With Big Data Support in Professional Social Networks

Chen, Wenbo; Zhou, Pan; Dong, Shaokang; Gong, Shimin; Hu, Menglan; Wang, Kehao; Wu, Dapeng

doi:10.1109/access.2018.2883953

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…Three contributions model the job recommendation problem with a somewhat different objective function, though, we still label these as MM-SE. Dong et al [38] and subsequent work [26] propose an MM-SE monolithic hybrid. Contrary to the approaches discussed so far, they consider the problem as a reinforcement learning problem.…”

Section: Model-based Methods On Shallow Embeddingsmentioning

confidence: 99%

Job Recommender Systems: A Review

Ruijt¹,

Bhulai²

2021

Preprint

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This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as 'hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.

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Section: Model-based Methods On Shallow Embeddingsmentioning

confidence: 99%

Job Recommender Systems: A Review

Ruijt¹,

Bhulai²

2021

Preprint

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“…Authors in [82] proposed to store summary cluster information of users (i.e., demographic information, location, and behavior) instead of recording the whole history of contexts and user feedback. The research proposed in [83] predicts a proper item by utilizing the feedback reward of previous users in the nearby context region. Similar items can be amalgamated into a cluster to reduce the computing load.…”

Section: ) Multidimensional-based Recommender Systemmentioning

confidence: 99%

“…Collaborative filtering [25], [30], [32], [36], [42], [43], [52], [58], [59], [63], [71], [72], [94], [95], [97], [101], [107], [108], [110] [42], [43], [52], [72], [75], [82], [83], [91], [97], [101], [105] Content based filtering [65], [113] Graph [88] [44], [53], [62], [77],…”

Section: Sparsitymentioning

confidence: 99%

“…Table III shows the distribution of studies based on the different techniques that are used in recommendation approaches. Techniques References Bayesian probability [80], [90] Bipartite graphs embedding [84] Clustering [30], [60], [70], [91], [107] Decision tree [26], [38], [83] Deep neural networks [35], [56], [59], [65], [71], [72], [73], [76], [93], [98], [99], [100], [108] Distance-based method [88], [89] Fuzzy clustering [113] K Nearest Neighbor [24], [25], [44], [54], [75]…”

Section: Sparsitymentioning

confidence: 99%

“…The study took place in an online environment and aimed to share knowledge between the users. The work in [83] addresses the enormous amount of data by leveraging users' explicit and implicit information to formulate user's space using a tree-based model that enables efficient computations through analyzing large-scale items at the cluster level. The tree-based model can handle the cold-start problem and expiring new items by formulating the context space and partitioning users' explicit information dynamically in each round.…”

Section: ) Collaborative Filtering Approachmentioning

confidence: 99%

See 2 more Smart Citations

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.

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e-Recruitment recommender systems: a systematic review

Freire

Castro

2020

Knowl Inf Syst

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Tree-Based Contextual Learning for Online Job or Candidate Recommendation With Big Data Support in Professional Social Networks

Cited by 11 publications

References 31 publications

Job Recommender Systems: A Review

Job Recommender Systems: A Review

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

e-Recruitment recommender systems: a systematic review

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