论文标题
基于标签嵌入的个性化点兴趣点建议系统
Tag Embedding Based Personalized Point Of Interest Recommendation System
论文作者
论文摘要
个性化的兴趣点建议对满足新地方的用户需求非常有帮助。在本文中，我们提出了一种基于标签的方法，用于个性化的兴趣点建议。我们对与兴趣点相对应的标签之间的关系进行建模。该模型以相关标签更接近的方式提供了代表性嵌入对应于标签。我们将基于兴趣点的兴趣点建模基于标签嵌入，并根据他们评级的兴趣点对用户（用户配置文件）进行建模。最后，我们基于用户嵌入和兴趣点的嵌入之间的余弦相似性对用户的候选兴趣点进行排名。此外，我们通过通过不同度量（例如NDCG@5，MRR，...）进行离散优化来找到对用户建模所需的参数。我们还分析了结果，同时考虑了每个用户的所有用户和各个参数的相同参数。除此之外，我们还分析了对结果的影响，同时更改数据集以建模标签之间的关系。我们的方法还将隐私泄漏问题最小化。我们使用了TREC上下文建议2016第2阶段数据集，并且对最新方法的所有措施都有显着改善。它将NDCG@5提高到12.8％，P@5升至4.3％，MRR提高了7.8％，这显示了该方法的有效性。
Personalized Point of Interest recommendation is very helpful for satisfying users' needs at new places. In this article, we propose a tag embedding based method for Personalized Recommendation of Point Of Interest. We model the relationship between tags corresponding to Point Of Interest. The model provides representative embedding corresponds to a tag in a way that related tags will be closer. We model Point of Interest-based on tag embedding and also model the users (user profile) based on the Point Of Interest rated by them. finally, we rank the user's candidate Point Of Interest based on cosine similarity between user's embedding and Point of Interest's embedding. Further, we find the parameters required to model user by discrete optimizing over different measures (like ndcg@5, MRR, ...). We also analyze the result while considering the same parameters for all users and individual parameters for each user. Along with it we also analyze the effect on the result while changing the dataset to model the relationship between tags. Our method also minimizes the privacy leak issue. We used TREC Contextual Suggestion 2016 Phase 2 dataset and have significant improvement over all the measures on the state of the art method. It improves ndcg@5 by 12.8%, p@5 by 4.3%, and MRR by 7.8%, which shows the effectiveness of the method.
