ReEL: Review Aware Explanation of Location Recommendation

By Ramesh Baral, Xiaolong Zhu, S. S. Iyengar, Tao Li. 2018

37 citations recorded by the source corpus.

The Location-Based Social Networks (LBSN) (e.g., Facebook, etc.) have many attributes (e.g., ratings, reviews, etc.) that play a crucial role for the Point-of-Interest (POI) recommendations. Unlike ratings, the reviews can help users to elaborate their consumption experience in terms of relevant factors of interest (aspects). Though some of the existing systems have exploited user reviews, most of them are less transparent and non-interpretable (as they conceal the reason behind recommendation). These reasons have motivated us towards explainable and interpretable recommendation. To the best of our knowledge, only a few of the researchers have exploited user reviews to incorporate the sentiment and opinions on different aspects for personalized and explainable POI recommendation. This paper proposes a model termed as ReEL (Review aware Explanation of Location Recommendation) which models the review-aspect correlation by exploiting deep neural network, formulates user-aspect bipartite relation as a bipartite graph, and models the explainable recommendation by using dense subgraph extraction and ranking-based techniques. The major contributions of this paper are: (i) it models users and POIs using the aspects posted on user reviews, and it provisions incorporation of multiple contexts (e.g., categorical, spatial, etc.) in POI recommendation, (ii) it formulates preference of users' on aspects as a bipartite relation, represents it as a location-aspect bipartite graph, and models the explainable recommendation with the notion of ordered dense subgraph extraction using bipartite cores, shingles, and ranking-based techniques, and (iii) it extensively evaluates the proposed models using three real-world datasets and demonstrates an improvement of 5.8% to 29.5% on F-score metric, when compared to the relevant studies.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Chen
2. Yongfeng Zhang
3. Xiangnan He
4. Zheng Qin
5. Tat-seng Chua
6. Qingyao Ai
7. Xiang Wang
8. Hanxiong Chen

In Information Systems

1. Xu Chen
2. Yongfeng Zhang
3. Xiangnan He
4. Zheng Qin
5. Tat-seng Chua
6. Qingyao Ai
7. Xiang Wang
8. Hanxiong Chen

In Sociology and Political Science

1. Yongfeng Zhang
2. Xu Chen

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “Baral et al. (2018) proposed a dense subgraph extraction model based on user-aspect bipartite graphs for explainable location recommendation in location-based social networks.”