Chenrui Zhang
ABSTRACT
In this paper, we present an ensemble method for job recommendation to ACM RecSys Challenge 2016. Given a user, the goal of a job recommendation system is to predict those job postings that are likely to be relevant to the user1.
Firstly, we analyze the train dataset and find several interesting patterns. Secondly, we describe our solution, which is an ensemble of two filters, combining the merits of traditional collaborative filtering and content-based filtering. Our approach finally achieved a score of 1632828.82, ranked at the 10th place on the public leaderboard.
- S. T. Al-Otaibi. A survey of job recommender systems. International Journal of Physical Sciences, 7(29):5127--5142, 2012.Google Scholar
Cross Ref
- B. Kille and F. Abel. Engaging the Crowd for Better Job Recommendations. In Proceedings of Workshop on Crowdsourcing and Human Computation for Recommender Systems (CrowdRec 2015), Sept. 2015.Google Scholar
- T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.Google Scholar
- J. B. Schafer, J. Konstan, and J. Riedi. Recommender systems in e-commerce. In Electronic Commerce, 1999. Google ScholarDigital Library
- P. B. Thorat, R. Goudar, and S. Barve. Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4), 2015.Google ScholarCross Ref
- K. Wei, J. Huang, and S. Fu. A survey of e-commerce recommender systems. In 2007 International Conference on Service Systems and Service Management, pages 1--5, June 2007.Google ScholarCross Ref
Index Terms
- An ensemble method for job recommender systems
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Reviews
Salvatore F. Pileggi
Recommender systems play an important role in electronic commerce (e-commerce). By analyzing the relations that exist between items and users, these systems are able (i) to improve the performance of the mechanisms for searching/discovering information on large-scale spaces, and (ii) to allow commercial platforms to better identify potential target users. In this paper, the authors focus on job recommender systems, which aim at predicting the job offers that are likely to be of interest for a given user profile. The proposed solution combines the use of two different classic filters, namely collaborative and content-based filters. The authors report their experience at the RecSys Challenge 2016 competition, where their system, despite its relative simplicity, was ranked in the tenth place for its performance. The abstract looks a bit unusual, mentioning a numeric score related to an unspecified metric, and the keywords that were provided by the authors are probably not the most representative ones. Moreover, the introductory part could have been more comprehensive and better structured, in order to ensure a more self-contained contribution. Last, several references to concepts/solutions mentioned in the paper are missing. Online Computing Reviews Service
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