Towards a Reconciliation of Point-of-Interest Recommendation Systems Using Multi-Agent Systems

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Point-of-interest (POI) recommendation is a good way to help Smartphone users discover new places by exploiting their preferences and social networks. However, this technique, which is often based on collaborative filtering, suffers from cold-start problem due to insufficient interaction with POIs (ratings) and the absence of declared friendship or trust relationships between users. To address this problem, one solution is to launch several Recommendations Systems (RS) simultaneously, using all available sources of information within the social network, especially in the case of a new user or a new POI. In this paper, a Multi-Agent System for Reconciling POI Recommendation Algorithms (MSRPRA) is proposed to exploit (1) the power of Pearson similarity deduced from POI ratings through the RatAg agent, (2) the effectiveness of Jaccard similarity derived from existing friendship relationships exploited by the FrAg agent and the contribution of trust scores declared by users using the TrAg agent. The system then uses a coordinator agent to merge, using the Borda voting method, the POIs lists generated by the RatAg, FrAg, and TrAg agents. The experimental results show that MSRPRA outperforms approaches based solely on Pearson similarity, Jaccard similarity, or explicit trust. On average, the model improves Precision by 7.38% and Recall by 7.73%, while achieving better precision in terms of RMSE, these results confirm the effectiveness of multi-agent fusion using Borda voting in improving the relevance of recommendations and mitigating the cold-start problem.