Next location recommendation: a multi-context features integration perspective

Wei, Xuemei; Liu, Chunli; Liu, Yezheng; Li, Yang; Zhang, Kai

doi:10.1007/s11280-022-01126-y

Cited by 1 publication

(1 citation statement)

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“…RecPOID method (Safavi and Jalali, 2021) employed fuzzy c-mean clustering to find similar friends and a 10-layer CNN model to extrapolate the important features from spatio-temporal information. Wei et al (2023) proposed a multi-context-based next location recommendation (MCLR) model wherein the high-order location and location semantic graph were used to capture the locationlocation dependencies. The approach also utilized the preference modeling from a friend's perspective by including the selected peers only.…”

Section: Related Workmentioning

confidence: 99%

Recency-based spatio-temporal similarity exploration for POI recommendation in location-based social networks

Acharya,

Mohbey

2024

Front. Sustain. Cities

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Point-of-interest (POI) recommendation is one of the primary tasks of location-based social networks (LBSNs). With user data in bulk, extracting useful information and addressing issues such as data sparsity and cold-start problems looming large in collaborative filtering become difficult. One of the plausible solutions is to incorporate contextual information into the recommendation process. In this article, we propose a Recency-based Spatio-Temporal Similarity Exploration (RSTSE) for POI recommendation that utilizes the recency-based trust estimation among the prospective neighbors of the target user. The trust level is categorized into two heads: direct trust, which can be extracted from the peer group information of the user, and indirect trust, which is measured based on venue popularity, temporal recency, radial proximity, and transitivity. The approach consists of two phases. In the incipient phase, POIs are extracted based on the preferences of potential neighbors, including the users who are recognized peers, the users with similar visiting histories in the spatial and temporal context, and the users with friend-of-friend relations. The telic phase involves Neural Collaborative Filtering (NCF) to capture the linear and non-linear user–POI interactions better. RSTSE has been evaluated on three real-world datasets, namely, Gowalla, Foursquare, and Weeplaces, and the results suggest efficacy over other state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%