Personalised ranking with single source implicit information for recommendation tasks a similarity based Monte Carlo Bayesian Personalised Ranking

Background: A recommender algorithm’s main goal is to learn user preferences from the user-system interactions and provide a list of relevant items to the user. In information retrieval literature this problem is formulated as learning to rank (LtR) problem. Bayesian Personalized Ranking (BPR) [1] is one of the popular LtR approaches based on pair-wise comparison using single source implicit information. Aim: In this work, we aim to design a recommender system algorithm that generates accurate recommendations. The system should only use a single source implicit user preference information. This is possible through a good approximation of the posterior probability in BPR optimization function. Method: We proposed a Similarity based Monte Carlo approximate solution for the posterior probability in BPR. We used four datasets from different recommendation application domains to evaluate the performance of our proposed algorithm. The input data was pre-processed to match with the requirements of the algorithm. Result: The result of the analysis shows a significant improvement in terms of mean average precision (MAP) for our proposed algorithm compared with the BPR and another alternative extension to BPR. Conclusion: We conclude that the proposed approximate solution is successful in providing the most informative samples to approximate BPR posterior probability. This is confirmed by the significant improvement of the accuracy of the provided ranked list of items for the users. i