CLoSe: Contextualized Location Sequence Recommender
CLoSe: Contextualized Location Sequence Recommender
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DOI：
10.1145/3240323.3240410
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发表时间：
2018-09
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通讯作者：
Ramesh Baral;S. Iyengar;Tao Li;N. Balakrishnan
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作者：
Ramesh Baral;S. Iyengar;Tao Li;N. Balakrishnan
基于位置的社交网络（LBSN）（例如，Facebook等）在过去的十年中，已经探索了兴趣点（POI）推荐。许多现有的系统专注于推荐单个位置或列表，其可能在上下文上不连贯。在本文中，我们提出了一个模型，称为CLoSe（上下文位置序列推荐），生成上下文相关的POI序列相关的用户偏好。POI序列识别器在许多日常活动中很有帮助，例如，据我们所知，本文是第一个制定上下文POI序列推荐利用递归神经网络（RNN）。我们将签入上下文合并到隐藏层，将全局上下文合并到RNN的隐藏层和输出层。我们还证明了扩展的长短期记忆（LSTM）在序列生成中的效率。本文的主要贡献是：（i）它利用多上下文，个性化的用户偏好来制定上下文POI序列生成，（ii）它提出了RNN和LSTM的上下文扩展，这些扩展包含适用于POI和POI序列的不同上下文，以及（iii）当使用两个真实世界的数据集进行评估时，它证明了所提出的模型在对F1和NDCG指标上的显着性能增益。
The location-based social networks (LBSN) (e.g., Facebook, etc.) have been explored in the past decade for Point-of-Interest (POI) recommendation. Many of the existing systems focus on recommending a single location or a list which might not be contextually coherent. In this paper, we propose a model termed CLoSe (Contextualized Location Sequence Recommender) that generates contextually coherent POI sequences relevant to user preferences. The POI sequence recommenders are helpful in many day-to-day activities, for e.g., itinerary planning, etc. To the best of our knowledge, this paper is the first to formulate contextual POI sequence recommendation by exploiting Recurrent Neural Network (RNN). We incorporate check-in contexts to the hidden layer and global context to the hidden and output layers of RNN. We also demonstrate the efficiency of extended Long-short term memory (LSTM) in sequence generation. The main contributions of this paper are: (i) it exploits multi-context, personalized user preferences to formulate contextual POI sequence generation, (ii) it presents contextual extensions of RNN and LSTM that incorporate different contexts applicable to a POI and POI sequence, and (iii) it demonstrates significant performance gain of proposed model on pair-F1 and NDCG metrics when evaluated with two real-world datasets.