Disentangling Geographical Effect for Point-of-Interest Recommendation

Document Type

Article

Publication Date

8-1-2023

Abstract

Point-of-Interest (POI) recommendation has drawn a lot of attention in both academia and industry. It utilizes user check-in data, aiming at recommending unvisited POIs to users. To address the data-sparsity problem, geographical information of POIs is often incorporated into recommender systems. However, most of the existing approaches model geographical impact in an implicit way, in which geographical information is encoded as auxiliary vectors for learning unified representations of users and POIs. Following this paradigm, the embedding of POIs can not reflect geographical similarity directly; thus, an explicit modeling approach is needed as geography is of great importance in POI recommendation. To address challenges in disentangling geographical effect, we proposed a disentangled representation learning method named DIG (short for Disentangled embedding of user Interest and POIs' Geographical information). Aiming at decoupling the geographical factor and the user interest factor thoroughly, we first proposed a geo-constrained negative sampling strategy, which helps to find reliable negative samples for the two factors. Second, a geo-enhanced soft-weighted loss function was proposed to quantify the trade-off between the two factors in loss computation. Extensive experiments have been conducted on two real-world datasets, and results have demonstrated the significant improvement of DIG at 3.92% - 20.32% 3.92% - 20.32% on recall, and 2.53% - 11.48% 2.53% - 11.48% on hit ratio, compared with other state-of-the-art approaches.

Publication Source (Journal or Book title)

IEEE Transactions on Knowledge and Data Engineering

First Page

7883

Last Page

7897

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