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 con- ducted on a large-scale job portal. We analyse correlations between rat- ings and implicit signals to detect situations where members liked their suggestions. Results show that replies and bookmarks reflect preferences much better than clicks.
['Benjamin Kille', 'Fabian Abel', 'Balázs Hidasi', 'Sahin Albayrak']
Using Interaction Signals for Job Recommendations