Using Interaction Signals for Job Recommendations

Abstract

Job recommender systems depend on accurate feedback to improve their suggestions. Implicit feedback arises in terms of clicks, bookmarks and replies. We present results from a member inquiry conducted on a large-scale job portal. We analyse correlations between ratings and implicit signals to detect situations where members liked their suggestions. Results show that replies and bookmarks reflect preferences much better than clicks.

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Authors:
Benjamin Kille, Fabian Abel, Balazs Hidasi, Sahin Albayrak
Category:
Conference Paper
Year:
2015
Location:
International Conference on Mobile Computing, Applications, and Services