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.
翻译:个人化利益点建议非常有助于满足用户在新地点的需求。 在本条中, 我们提出一个基于标签嵌入的嵌入方法, 用于个人化利益点建议的个人化建议。 我们用与利益点相对应的标签建立关系模式。 模型提供代表嵌入对应标签的方式, 相关标签将更加接近。 我们以标签嵌入为基础, 并以用户的“利益点” 评分为基础, 并模拟用户( 用户概况) 。 最后, 我们根据用户嵌入与利益点嵌入之间的相似性, 对用户的候选利益点进行排序。 此外, 我们通过离散优化不同措施( 如 ndcg@5, MRR,...) 来找到模型用户所需的参数。 我们还分析结果, 同时考虑到所有用户的相同参数和每个用户的单个参数。 同时, 我们还分析结果的效果, 修改数据集以模拟标记之间的关系。 我们的方法还尽量减少隐私泄漏问题。 我们使用TREC背景建议 2016 阶段2 数据集, 并且通过离散优化不同措施( 如ncg@ pr%) 改进了所有措施的参数。