Abstrakt
In this paper, we present our work towards comparing on-line and off-line evaluation metrics in the context of small e-commerce recommender systems. Recommending on small e-commerce enterprises is rather challenging due to the lower volume of interactions and low user loyalty, rarely extending beyond a single session.
On the other hand, we usually have to deal with lower volumes of objects, which are easier to discover by users through various browsing/searching GUIs. The main goal of this paper is to determine applicability of off-line evaluation metrics in learning true usability of recommender systems (evaluated on-line in A/B testing).
In total 800 variants of recommenders were evaluated off-line w.r.t. 18 metrics covering rating-based, ranking-based, novelty and diversity evaluation. The off-line results were afterwards compared with on-line evaluation of 12 selected recommender variants and based on the results, we tried to learn and utilize an off-line to on-line results prediction model.
Off-line results shown a great variance in performance w.r.t. different metrics with the Pareto front covering 64% of the approaches. Furthermore, we observed that on-line results are considerably affected by the seniority of users.
On-line metrics correlates positively with ranking-based metrics (AUC, MRR, nDCG) for novice users, while too high values of novelty had a negative impact on the on-line results for them.