Zero-Injection Meets Deep Learning: Boosting the Accuracy of Collaborative Filtering in Top-N Recommendation

Abstract

Zero-Injection has been known to be very effective in alleviating the data sparsity problem in collaborative filtering (CF), owing to its idea of finding and exploiting uninteresting items as users’ negative preferences. However, this idea has been only applied to the linear CF models such as SVD and SVD++, where the linear interactions among users and items may have a limitation in fully exploiting the additional negative preferences from uninteresting items. To overcome this limitation, we explore CF based on deep learning models which are highly flexible and thus expected to fully enjoy the benefits from uninteresting items. Empirically, our proposed models equipped with Zero-Injection achieve great improvements of recommendation accuracy under various situations such as basic top-N recommendation, long-tail item recommendation, and recommendation to cold-start users.