A Ranker Ensemble for Multi-objective Job Recommendation in an Item Cold Start Setting

Yağcı, A. Murat; Gürgen, Fikret

doi:10.1145/3124791.3124798

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…The problem has also been addressed from a learning to rank perspective, using LambdaMART [54]. Besides boosting, also refinements using common CBR or CF methods have been proposed [30,124,82] Gui et al [46] propose the so-called benefit-cost model, a re-ranking procedure for recommender systems that takes into account downside in recommender systems, and which is validated on a job recommender problem. To our knowledge, this contribution is the only contribution that raises the question of whether in some scenarios a recommendation should be given at all.…”

Section: Model-based Methods On Shallow Embeddingsmentioning

confidence: 99%

“…For Type 2 behavioral feedback, common strategies for defining negatives include using shown but skipped items [54,59,70,79,89,91,94,99,101,120,126,130,133,47], picking negative samples at random (not per se uniform) [13,124,125,73,29], replacing the job (but not the candidate and further context) at random [132], using vacancies of which the vacancy details were shown, but did not lead to an application [101,100], or if the method allows for sparse matrices (such as in some matrix factorization methods): using all possible vacancy-user interactions [74,98,71,68,19,102,4,72,80,22,15,83,105]. Others incorporate negative sampling into the estimation method itself [76,14].…”

Section: Choices In Negative Samplingmentioning

confidence: 99%

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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%

Section: Choices In Negative Samplingmentioning

confidence: 99%

Job Recommender Systems: A Review

Ruijt¹,

Bhulai²

2021

Preprint

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“…Baselines. In this paper, two representative course recommendation methods, namely, hybrid [39] and ensemble [40], were adopted to verify the effectiveness of the proposed method. The two models are briefly introduced below.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Gao

Luo

2022

Wireless Communications and Mobile Computing

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Massive Open Online Course (MOOC) has been criticized for low completion rates, and one of the major reasons is that it fails to offer personalized course recommendations for different users with different demands. To solve this problem, this paper proposes a personalized course recommendation model based on convolutional neural network combined with negative sequence pattern mining. The model first models the course-learning sequence as a negative sequence pattern according to the user’s course registration, degree of completion, and final grades, in which, the negative term means that students should not choose and misoperate the principle of courses. Then, it employs a convolutional neural network structure to extract the internal features of negative sequence patterns for representation learning. Finally, through the convolutional sequence-embedding model, each user is recommended with a course list that includes the user’s maximized needs in recent temporal terms and the courses that are easy to be misselected. Experiment results show that the recommended model achieves higher recommendation performance with lower course dropout rate compared to baselines, which provides a new insight for both online and offline course recommendation.

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“…A very common issue that personalization systems suffer from is the cold-start [5]. The cold-start problem arises in two cases:…”

Section: Cold-start Problemmentioning

confidence: 99%

“…Ensembler-Ensembling various models and combining the predictions of different models is a technique that is used by many researchers for improving results [5,35]. In our case, an average-based ensembler has been built.…”

mentioning

confidence: 99%

A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising Systems

Viktoratos

Tsadiras

2022

Algorithms

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A domain that has gained popularity in the past few years is personalized advertisement. Researchers and developers collect user contextual attributes (e.g., location, time, history, etc.) and apply state-of-the-art algorithms to present relevant ads. A problem occurs when the user has limited or no data available and, therefore, the algorithms cannot work well. This situation is widely referred in the literature as the ‘cold-start’ case. The aim of this manuscript is to explore this problem and present a prediction approach for personalized mobile advertising systems that addresses the cold-start, and especially the frozen user case, when a user has no data at all. The approach consists of three steps: (a) identify existing datasets and use specific attributes that could be gathered from a frozen user, (b) train and test machine learning models in the existing datasets and predict click-through rate, and (c) the development phase and the usage in a system.

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A Ranker Ensemble for Multi-objective Job Recommendation in an Item Cold Start Setting

Cited by 9 publications

References 5 publications

Job Recommender Systems: A Review

Job Recommender Systems: A Review

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising Systems

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