LAUSR

The way that computing systems digest geographic space is fundamentally different from people’s understanding of space. In human discourse, a geographic space is referred to by a place name, and the reasoning about a place are based on its characteristics. This is in contrast with computing systems where geographical spaces are handled by the definition of coordinate systems. Hence, when recommending places, a recommendation method that leverages textual content, as a medium of communication among people, can be better understood. In this paper, we use elements of Natural Language Processing (NLP), such as Positive Point-wise Mutual Information (PPMI), Term Frequency - Inverse Document Frequency (TF-IDF), and Multi-Dimensional Scaling (MDS), to infer a conceptual space of the items of a place-based recommender system. By applying a Support Vector Machine (SVM) classifier on the resulting conceptual space, some meaningful directions are extracted. Shannon entropy is used as a measure to identify the directions that imply a valid geographic region. We apply the method on a dataset of advertisement descriptions of rental properties and a dataset of Persian Wikipedia articles. The results showed the proposed method is able to measure the similarity of items in the inferred conceptual space with 88% of accuracy. A comparison with BERT algorithm demonstrates the superiority of the proposed method over the baseline models.