Content-Based Approaches for Cold-Start Job Recommendations

Mattia Bianchi, Federico Cesaro, Filippo Ciceri, Mattia Dagrada, Alberto Gasparin, Daniele Grattarola, Ilyas Inajjar, Alberto Maria Metelli, and Leonardo Cella

Proceedings of the Recommender Systems Challenge 2017, 2017.

This paper provides an overview of the approach we adopted as team Lunatic Goats for the ACM RecSys Challenge 2017 [7]. The competition, organized by, focuses on a cold start job recommendation scenario. The goal was to design and tune a recommendation system able to predict past users’ interactions, for the offline stage, and to provide recommendations pushed every day to real users through the XING portal, for the online stage. Our strategy, which saw models coming from different techniques combined in a multi-layer ensemble, granted us the first place in the offline part and the qualification as second best team in the final leaderboard. All our algorithms mainly resort to content-based approaches, that, thanks to its ability to provide good recommendations even for cold-start items allowed us, quite unexpectedly, to achieve good results in terms of prediction quality and computational time.

[Paper] [BibTeX]

    author = "Bianchi, Mattia and Cesaro, Federico and Ciceri, Filippo and Dagrada, Mattia and Gasparin, Alberto and Grattarola, Daniele and Inajjar, Ilyas and Metelli, Alberto Maria and Cella, Leonardo",
    title = "Content-Based Approaches for Cold-Start Job Recommendations",
    booktitle = "Proceedings of the Recommender Systems Challenge 2017",
    series = "RecSys Challenge '17",
    year = "2017",
    isbn = "978-1-4503-5391-5",
    location = "Como, Italy",
    pages = "6:1--6:5",
    articleno = "6",
    numpages = "5",
    url = "",
    doi = "10.1145/3124791.3124793",
    acmid = "3124793",
    publisher = "ACM",
    address = "New York, NY, USA",
    keywords = "ACM RecSys Challenge 2017, Cold-Start recommendations, Content-Based Filtering, Job recommendations, Recommendation Systems"