Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Yanli Jia
DOI: 10.4018/IJITSA.325790
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Abstract

Aiming at the problems resulting from the fact that the existing point of interest (POI) recommendation methods cannot effectively consider the personalized differences of users' mobile behavior in space and time, the author proposes a personalized POI recommendation method using attention-based time sequence and distance contexts gated recurrent unit (ATSD-GRU). First, the author combined the time sequence and distance context with the GRU to extract useful information from users, effectively alleviating the data sparsity. Second, inspired by the attention mechanism, the author introduced the attention model further into the neural network to capture the user's main mobile behavior intention. Finally, the author studied the ATSD-GRU and trained through Bayesian personalized sorting framework and back propagation algorithm. Experiments imply that the proposed method outperforms the comparison method in terms of the F1 index for any recommended number. When the recommendation list length is 15, the proposed algorithm exhibits an accuracy of 9.23% and a recall rate of 14.65%, both higher than the compared algorithm.
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Introduction

The process of development and protection of tourism resources is a systematic project related to future generations that involves transportation, logistics, and other industrial chains. Adhering to the people-oriented principle and promoting the development of this cause from the vantage point of building a harmonious society healthily and sustainably are necessary. A location-based service (LBS) is an information service that provides various location-related services according to the location of users or devices (Qian et al., 2013; Miao et al., 2021). As the popularity of smart phones, tablets and other mobile intelligent terminals increases, the service mode of location services has changed dramatically, and the service content has also been greatly enriched (Kim et al., 2002; Song et al., 2006). An LBS based on mobile terminals is also defined as a process of providing specific value-added services to users based on communication networks by sensing users’ space, time, and other contextual information through mobile terminals and combining user preferences (Chu, & Park, 2009; Wang et al., 2018).

The provided services focus on users’ location information, so they are called location-based services (Tian et al., 2019). The application system provides users with personalized services for different scenarios in real time by sensing the users’ current location, historical space-time context, and other information. The value of an LBS lies in using information and communication technologies to build a bridge between the real and virtual worlds for users through various intelligent terminal devices (Sahba et al., 2014). With its computing transparency, seamless mobility, universality of information access, and intelligence based on context awareness, an LBS realizes user-centric information services (Sahba et al., 2018; Wen et al., 2012). At present, the main application fields of an LBS include electronic map and navigation, transportation and logistics, daily travel and tourism, location social network recommendation, e-commerce and mobile advertising, entertainment and fitness, and mobile resource management.

The goal of a personalized tourism location service is to use mobile terminals and related information technology to obtain tourist behavior and depict tourist preferences through data mining and feature representation technology. Personalized recommendation technology is then used to provide tourists with information and services related to tourism location that cater to individual preferences and tourism situations (Zhang et al., 2014).

In practical application, personalized location services for tourism include tour route recommendation and Point of Interest (POI) access (Cui et al., 2020; Fard et al., 2019). The goal of tour route recommendation is to generate a valuable route for tourists according to their personal preferences and tour context constraints. Tour route recommendation scenarios include suggestions for a specific tour to scenic spots and suggestions for routes for scenic spots in cities or regions (Gupta et al., 2020; Zhou, & Han, 2019). The purpose of POI visit recommendation is to suggest a list of potential POI visits or predict the next possible POI visit for tourists according to their historical visit preferences before or during the trip (Choi et al., 2006; Ardissono et al., 2003).

It is convenient for travelers to obtain tourism location information and services owing to the widespread use of the internet and intelligent terminals in tourism activities (Wang, & Shao, 2004). However, because of the relatively lagging technologies and methods of personalized LBS for tourism or urban travel, low-quality location-based information services lead to the issue of tourism information overload for tourists on the mobile end; this issue not only affects mobile users’ tourism experience but also hinders the further development of tourism-related industries (Guo, & Lu, 2007).

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