Combining complementary diversification models for personalized POI recommendations

Heitor Werneck, N. Silva, Fernando Mourão, A. Pereira, L. Rocha
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引用次数: 1

Abstract

Location-Based Social Networks (LBSNs) have become important tools for people interested in exploring new places. And, similar to traditional recommendation domains, handling the trade-off between accuracy and diversity is a major challenge to provide useful recommendations. However, this domain adds an equally relevant dimension to this challenge: the geographical distance between users and each point-of-interest (POI). Besides understanding how the characteristics of services offered by each POI fit the user needs, realizing how far users are willing to move to fulfill these needs is of paramount relevance. Moreover, the users present distinct levels of interest in diversification. In this paper, we propose a strategy to provide POI recommendations linearly combining categorical and geographical diversifications in a personalized way. Indeed, our strategy is able to prioritize quality dimensions that better suit the personalized needs of each user, with gains up to 10% when compared with unpersonalized versions.
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结合互补的多样化模型,个性化推荐POI
基于位置的社交网络(LBSNs)已经成为人们探索新地方的重要工具。而且,与传统的推荐领域类似,处理准确度和多样性之间的权衡是提供有用推荐的主要挑战。然而,这个领域为这一挑战增加了一个同样相关的维度:用户和每个兴趣点(POI)之间的地理距离。除了了解每个POI提供的服务的特征如何满足用户需求之外,了解用户愿意在多大程度上满足这些需求也是至关重要的。此外,用户对多样化表现出不同程度的兴趣。在本文中,我们提出了一种策略,以个性化的方式将类别多样化和地理多样化线性结合起来提供POI建议。事实上,我们的策略能够优先考虑更适合每个用户个性化需求的质量维度,与非个性化版本相比,收益高达10%。
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