A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

Abstract

As the popularity of Location-based Social Networks (LBSNs) increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user’s main activity location, and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation. However, previous research does not consider the impact of the combination of these different factors. In this paper, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

“…Our future plans include the incorporation of deep learning, graph-based, sequential, and sessions-based models as proposed in works like [1,9]. These models can integrate various contextual components like geographical, temporal, social, and categorical relevance scores using fusion rules such as product or sum [8,12], forming a unified preference score [10,13,16]. Contextual information, denoted by 𝑐 𝑖 , can be infused using a polynomial regression model 𝑟𝑒𝑐 𝑢,𝑝 " Λ ¨C `Λpair ¨Cpair `𝜆123 𝑐 1 𝑐 2 𝑐 3 (1) where:…”

“…Other types of context may include social ties, the category of POIs, comments on POIs, etc. Previous research works such as [12] have shown the way incorporating these rich contexts information have a significant impact on the performance of POI recommendation models. Therefore, in recent years, there has been a growth in the demand for specialized recommendation algorithms and methodologies that can incorporate and fuse contextual information into the POI recommendation process [24]; arXiv:2306.11395v1 [cs.IR] 20 Jun 2023…”

Next location recommendation: a multi-context features integration perspective

“…Research shows that contextual information plays an important role in location recommendation tasks [28] and thus context-based location recommendation models focusing on point-of-interest (POI) are gradually developed. For example GT-HAN [21] used three factors (the geo-influence of POIs, the geo-susceptibility of POIs, and the distance between POIs) to model the geographical co-influence between two POIs, and applied attention network to integrate those features for location recommendation.…”

“…Therefore, we believe that preference consis- tency between users, rather than friendship itself, determines the behavior similarity between users. -Third, although various embedding methods with auxiliary information have been proposed to learn the representations that capture users' sequential movements [47,27,38,37], their ability to learn complex patterns, especially hierarchical structures, is limited [9,28]. Meanwhile, contextual information from distinctive sources is assembled and mapped to a uniform space for representation learning [2,40], which limits the model's capacity to exploit the fine-grained intrinsic interactions between contexts for better performance.…”

Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses

“…In contrast, POI data have the advantages of simple acquisition and convenient processing, meaning that POI data are widely used in geographical research. At present, the research on POI mainly focuses on a recommendation model [19][20][21] and urban functional zoning [22], and the research on POI itself mainly focuses on automatic classification [23] and classification accuracy by using machine learning. Nak introduced POI into eye-tracking data analysis [24], making the research scope of POI broader.…”