Popularity Of Point Of Interest Research Articles

Recommendation rules to personalize itineraries for tourists in an unfamiliar city

Currently, tourism has become increasingly related to new technologies. In this context, itinerary planning and trip recommendation are challenging tasks for tourists with different profiles. Tourists are generally not familiar with different Points-of-Interest (POIs) in a new city. To this end, they need to select and organize POIs that align with their interest preferences and trip constraints such as departure location, available trip duration, etc. as an itinerary. In this paper, we propose to extract recommendation rules using Multi-Objective Evolutionary Algorithms (MOEAs), to find a trade-off between two objectives: (1) maximizing the POIs popularity, and (2) maximizing the time user interest. We conducted a comparative study of several Multi-Objective Evolutionary Algorithms (MOEAs), such as NSGAII, SPEA2, and IBEA, based on the Flickr dataset of different cities. Our findings confirm the efficiency of NSGA-II in generating recommendation rules to personalize itineraries for tourists in unfamiliar cities.

Personalized itinerary planning using an adaptive genetic algorithm

Planning an itinerary for travelling can be tedious, time-consuming, and challenging. This is especially true for tourists who have limited time budgets and are unfamiliar with a wide range of Points-of-Interest (POIs) in a city. To address this challenge, this paper proposes an Adaptive Genetic Algorithm (AGA) for personalized itinerary planning. This approach considers travelers' preferences, such as mandatory POIs, total number of POIs, POI popularity, POI cost, and POI rating. It views the itinerary planning problem as a multi-objective optimization problem and proposes an Adaptive Genetic Algorithm (AGA) to solve this problem. The results show that the AGAM algorithm is a promising approach for personalized itinerary planning. It is able to find itineraries that meet the traveler's preferences that are efficient in terms of time, cost, and overall rating.

Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion

Trip recommendation is a popular and significant location-aware service that can help visitors make more accurate travel plans. Its principal purpose is to provide a sequence of points of interest (POIs) for a visitor who has a specific demand, such as the number of attractions, start location, end location, etc. Previous studies either regard the trip recommendation task as a simplistic orienteering problem or endeavor to maximize the users’ visiting preferences and transitional regularities in a multi-round iteration manner. However, significant contexts such as long-term transitional dependencies and spatial–temporal correlations are under-explored. Although the emerging deep recursive models (e.g., recurrent neural networks) enable us to relieve the above limitations, there still exist three major challenges: (1) most conventional offline-homogeneous knowledge distillation approaches for key entities (e.g, POI and visiting time) could lead to the inadequacy of capturing the inherent heterogeneous interactions among the different entities and even result in the deviation of understanding human real individual preferences; (2) data sparsity in transitional regularity learning impedes the ability to comprehend human diverse mobility patterns; and (3) the lack of considering contextual facts such as attractive bias would degrade the generalization ability of the model. To remedy the above issues, this work presents a novel framework based on spatial–temporal graph representation learning, namely GraphTrip. Specifically, we first introduce a location-aware information fusion after building the spatial–temporal graph (ST-Graph). Then, a dual-grained human mobility learning module is proposed to address the sparsity of periodic regularity. In addition, we fuse the explicit information (e.g., POI popularity) behind the trip data as prior knowledge to facilitate the performance of trip inference. In the end, the experimental results conducted on five real-world trip datasets demonstrate our proposed GraphTrip achieves promising gains against several cutting-edge baselines, e.g., up to 5.75% and 4.45% improvements on Toronto regarding F1 and pairs-F1.

Precision Marketing Strategy for Ecotourism Based on Data Mining and User Images

With the continuous development of ecotourism industry, it has become a common concern in ecotourism industry to utilize the available data to realize the change of ecotourism industry and promote the product development and business expansion of ecotourism industry. To this end, we propose a personalized ecotourism route recommendation (PTIR) algorithm based on the popularity of points of interest (POI) and users’ interest preferences. First, we analyze the real historical ecotourism footprints of users, propose a time-based user interest preference, and design a method to calculate the best ecotourism route given the travel time limit, departure point, and end point. Experiments were conducted on a real dataset of the Flickr social networking site, and the results showed that this personalized ecotourism route recommendation algorithm has greatly improved accuracy and recall compared with the traditional algorithm that only considers POI popularity, and the results were visualized through specific experimental cases based on user image clustering.

Efficient itinerary recommendation via personalized POI selection and pruning

Personalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to construct an itinerary based on visitors’ interest and preference, time budget and uncertain queuing time. Previous studies typically aim to plan itineraries that maximize POI popularity, visitors’ interest and minimize queuing time. However, existing solutions may not reflect visitor preferences because when creating itineraries, they prefer to recommend POIs with short prior visiting periods. These recommendations can conflict with real-life scenarios as visitors typically spend less time at POIs that they do not enjoy, thus leading to the inclusion of unsuitable POIs. Moreover, constructing itineraries based on selected POIs is a challenging and time-consuming process. Existing approaches involve searching through a large number of non-optimal, duplicate itineraries that are time-consuming to review and generate. To address these issues, we propose an adaptive Monte Carlo tree search (MCTS)-based reinforcement learning algorithm EffiTourRec using an effective POI selection strategy by giving preference to POIs with long visiting times and short queuing times along with high POI popularity and visitor interest. In addition, to reduce non-optimal and duplicated itineraries generation, we propose an efficient MCTS search pruning technique to explore a smaller, more promising portion of solution space. Experiment results in real theme park datasets show clear advantages of our proposed method over baselines, where our method outperforms the current state-of-the-art by 20.89 to 52.32% in precision, 8.36 to 21.35% in F1-score and 40.00 to 67.64% in execution time.

Decentralized Intelligent Search of Tourist Routes Based on Check-In Data

The location social network generates a large amount of data; these dada reflect the user's preferences and the popularity of the route, and a new model is provided for travel route search. Based on this demand, a problem of local distributed travel route search is proposed for group users. In this problem, the personal preferences of group users are combined, and an access route is found with partial POI (point of interest) and the largest group profit. The check-in data are used to generate a POI transfer relationship diagram based on the user's transfer between POIs, and route search is performed on the relationship diagram. In order to improve the search efficiency, a two-layer transfer relationship diagram is designed according to the popularity and transfer relationship of POI, the POI is generalized, and a hierarchical query is realized. A branch and bound search strategy optimization algorithm is designed, and the control relationship between nodes is used for pruning; the search efficiency of the algorithm is further improved.

Pattern-based dual learning for point-of-interest (POI) recommendation

PurposeTourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.Design/methodology/approachThe authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.FindingsThe authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.Originality/valueThis study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation

The popularity of location-based social networks (LBSNs) has led to a tremendous amount of user check-in data. Recommending points of interest (POIs) plays a key role in satisfying users needs in LBSNs. While recent work has explored the idea of adopting collaborative ranking (CR) for recommendation, there have been few attempts to incorporate temporal information for POI recommendation using CR. In this article, we propose a two-phase CR algorithm that incorporates the geographical influence of POIs and is regularized based on the variance of POIs popularity and users activities over time. The time-sensitive regularizer penalizes user and POIs that have been more time-sensitive in the past, helping the model to account for their long-term behavioral patterns while learning from user-POI interactions. Moreover, in the first phase, it attempts to rank visited POIs higher than the unvisited ones, and at the same time, apply the geographical influence. In the second phase, our algorithm tries to rank users favorite POIs higher on the recommendation list. Both phases employ a collaborative learning strategy that enables the model to capture complex latent associations from two different perspectives. Experiments on real-world datasets show that our proposed time-sensitive collaborative ranking model beats state-of-the-art POI recommendation methods.

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Traveling as a very popular leisure activity enjoyed by many people all over the world. Typically, tourists have different kinds of preferences about their itineraries, limited time budgets, unfamiliar with the wide range of Points-of-Interest (POIs) in a city, so that planning an itinerary is quite tedious, time-consuming, and challenging for them. In this paper, we propose an adaptive genetic algorithm for personalized itinerary planning for travelers to plan their itineraries better. Firstly, desired starting POIs (e.g., POIs that are close to their hotels) and destination POIs (e.g., POIs that are near train stations or airports) are considered in our approach. Secondly, we also take some general factors into account that travelers would consider in their preferences of an itinerary, which are mandatory POIs, the total number of POIs, the overall POI popularity, the overall cost, and the overall rating. Thirdly, we view this kind of recommendation task as a Multi-Objective Optimization problem, and we propose an adaptive genetic algorithm with the crossover and mutation probabilities (AGAM) for solving this problem to better find the best global solution. Fourthly, we allocate different weights to every factor which considered in our paper to generate a personalized itinerary recommendation for better meet many kinds of preferences of tourists. Finally, we compare our approach against baselines on real-world datasets which include six touristic cities, and the experimental results show that the AGAM achieves better recommendation performance in terms of the mandatory POIs, total POI visits, overall POI popularity, total travel time (including travel time and visit duration), overall cost, and overall rating.

Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks

Point-of-Interest (POI) recommendation is one of the most essential tasks in LBSNs to help users discover new interesting locations, especially when users travel out of town or to unfamiliar areas. Current studies on POI recommendation in LBSNs mainly focus on modeling multiple factors extracted from users’ profiles and checking-in records. Data sparsity and incompleteness of user-POI interaction matrix are very common problems in POI recommendation, especially for the out-of-town scenario. Another challenge is that most information in the LBSNs is unreliable due to users’ different backgrounds or preferences. Because of the close relationship between users, information from trustable friends on Communication-Based Social Networks (CBSNs) is more valuable than that in LBSNs, which can give a preferable suggestion instead of trustless reviews in LBSNs. In this study, we propose a latent probabilistic generative model called HI-LDA (Heterogeneous Information based LDA), which can accurately capture users’ words on CBSNs by taking into full consideration the information on LBSNs including geographical effect as well as the abundant information including social relationship, users’ interactive behaviors and comment content. In particular, the parameters of the HI-LDA model can be inferred by the Gibbs sampling method in an effective fashion. Beyond these proposed techniques, we introduce an POI recommendation framework integrating geographical clustering approach considering the locations and popularity of POIs simultaneously. Extensive experiments were conducted to evaluate the performance of the proposed framework on two real heterogeneous LBSN-CBSN networks. The experimental results demonstrate the superiority of HI-LDA on effective and efficient POI recommendation in both home-town and out-of-town scenarios, when compared with the state-of-the-art baseline approaches.

Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information

Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scene spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion ( HMCF ), for predicting the popularity of POIs. Specifically, in order to cope with the problem of data sparsity, we propose to comprehensively describe POI using various types of user generated content (UGC) (e.g., text and image) from multiple sources. Then, we devise an effective POI modelling method in a hierarchical manner, which simultaneously injects semantic knowledge as well as multi-clue representative power into POIs. For evaluation, we construct a multi-source POI dataset by collecting all the textual and visual content of several specific provinces in China from four main-stream tourism platforms during 2006 to 2017. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions’ popularity as compared to several baseline methods.

Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select points of interest (POI) to visit in unfamiliar cities and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photographs. Our tour recommendation problem is modeled using a formulation of the Orienteering problem and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations and demonstrate how POI visit duration can be personalized using this time-based user interest. Furthermore, we demonstrate how PersTour can be further enhanced by: (i) a weighted updating of user interests based on the recency of their POI visits and (ii) an automatic weighting between POI popularity and user interests based on the tourist’s activity level. Using a Flickr dataset of ten cities, our experiments show the effectiveness of PersTour against various collaborative filtering and greedy-based baselines, in terms of tour popularity, interest, recall, precision and F $$_1$$ -score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation

The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through Point-of-Interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper, we propose a context-aware probabilistic matrix factorization method for POI recommendation. Our model is called TGSC-PMF, it exploits textual information, geographical information, social information, categorical information and popularity information, and incorporates these factors effectively. First, we exploit an aggregated Latent Dirichlet Allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and then generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, we model the categorical correlations which combine the category bias of users and the popularity of POIs into categorical relevance score. Further, we integrate the interest, geographical, social and categorical relevance scores into probabilistic matrix factorization model (PMF) for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGSC-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.