With this assumption, we have a new criterion called Medical Bayesian Personalized Ranking over multiple users' actions (MBPR).
The algorithm steps of MBPR are depicted in Algorithm 1, where m is the number of users and n is the number of items.
The computational time of learning the MBPR model is mainly taken by evaluating the objective function and its gradients against feature vectors (variables).
The experimental results of MBPR and other baselines on two real-world datasets are presented in Table 3 and Table 4, and the results of NDCG on Topmd-A and Sobazaar-P are shown in Figure 2, from which we can have the following observations:
(1) For both datasets, BPR and MBPR are much better than the random algorithm, which shows the effectiveness of pairwise preference assumptions.
From the percentage of improvements on all the evaluation metrics that MBPR achieves relative to the other models in Tables 3 and 4, it clearly indicates that MBPR shows more significant improvement on Sobazaar-P than Topmd-A.
And thus, the results clearly indicate superior prediction ability of MBPR in various application scenarios.
In this paper, we studied the one-class collaborative filtering problem and designed a novel algorithm called Medical Bayesian Personalized Ranking over multiple users' actions (MBPR).
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