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In recent years, neural models have been repeatedly touted to exhibitstate-of-the-art performance in recommendation. Nevertheless, multiple recentstudies have revealed that the reported state-of-the-art results of many neuralrecommendation models cannot be reliably replicated. A primary reason is thatexisting evaluations are performed under various inconsistent protocols.Correspondingly, these replicability issues make it difficult to understand howmuch benefit we can actually gain from these neural models. It then becomesclear that a fair and comprehensive performance comparison between traditionaland neural models is needed.  Motivated by these issues, we perform a large-scale, systematic study tocompare recent neural recommendation models against traditional ones in top-nrecommendation from implicit data. We propose a set of evaluation strategiesfor measuring memorization performance, generalization performance, andsubgroup-specific performance of recommendation models. We conduct extensiveexperiments with 13 popular recommendation models (including two neural modelsand 11 traditional ones as baselines) on nine commonly used datasets. Ourexperiments demonstrate that even with extensive hyper-parameter searches,neural models do not dominate traditional models in all aspects, e.g., theyfare worse in terms of average HitRate. We further find that there are areaswhere neural models seem to outperform non-neural models, for example, inrecommendation diversity and robustness between different subgroups of usersand items. Our work illuminates the relative advantages and disadvantages ofneural models in recommendation and is therefore an important step towardsbuilding better recommender systems.

When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?