Sequence Of Points Of Interest Research Articles

A BiLSTM-attention-based point-of-interest recommendation algorithm

Abstract Aiming at the problem that users’ check-in interest preferences in social networks have complex time dependences, which leads to inaccurate point-of-interest (POI) recommendations, a location-based POI recommendation model using deep learning for social network big data is proposed. First, the original data are fed into an embedding layer of the model for dense vector representation and to obtain the user’s check-in sequence (UCS) and space-time interval information. Then, the UCS and spatiotemporal interval information are sent into a bidirectional long-term memory model for detailed analysis, where the UCS and location sequence representation are updated using a self-attention mechanism. Finally, candidate POIs are compared with the user’s preferences, and a POI sequence with three consecutive recommended locations is generated. The experimental analysis shows that the model performs best when the Huber loss function is used and the number of training iterations is set to 200. In the Foursquare dataset, Recall@20 and NDCG@20 reach 0.418 and 0.143, and in the Gowalla dataset, the corresponding values are 0.387 and 0.148.

Next POI Recommendation with Dynamic Graph and Explicit Dependency

Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services. Its main objective is to predict the user's next interested POI based on her previous check-in information. Most existing methods directly use users' historical check-in trajectories to construct various graphs to assist sequential models to complete this task. However, as users' check-in data is extremely sparse, it is difficult to capture the potential relations between POIs by directly using these check-in data. To this end, we propose the Sequence-based Neighbour search and Prediction Model (SNPM) for next POI recommendation. In SNPM, the RotatE knowledge graph embedding and Eigenmap methods are used to extract POI relationships implied in check-in data, and build the POI similarity graph. Then, we enhance the model's generalized representations of POIs' general features by aggregating similar POIs. As the context is typically rich and valuable when making Next POI predictions, the sequence model selects which POIs to aggregate not only depends on the current state, but also needs to consider the previous POI sequence. Therefore, we construct a Sequence-based, Dynamic Neighbor Graph (SDNG) to find the similarity neighbourhood and develop a Multi-Step Dependency Prediction model (MSDP) inspired by RotatE, which explicitly leverage information from previous states. We evaluate the proposed model on two real-world datasets, and the experimental results show that the proposed method significantly outperforms existing state-of-the-art POI recommendation methods.

Dynamic Personalized POI Sequence Recommendation with Fine-Grained Contexts

The Point Of Interest (POI) sequence recommendation is the key task in itinerary and travel route planning. Existing works usually consider the temporal and spatial factors in travel planning. However, the external environment, such as the weather, is usually overlooked. In fact, the weather is an important factor because it can affect a user’s check-in behaviors. Furthermore, most of the existing research is based on a static environment for POI sequence recommendation. While the external environment (e.g., the weather) may change during travel, it is difficult for existing works to adjust the POI sequence in time. What’s more, people usually prefer the attractive routes when traveling. To address these issues, we first conduct comprehensive data analysis on two real-world check-in datasets to study the effects of weather and time, as well as the features of the POI sequence. Based on this, we propose a model of Dynamic Personalized POI Sequence Recommendation with fine-grained contexts ( DPSR for short). It extracts user interest and POI popularity with fine-grained contexts and captures the attractiveness of the POI sequence. Next, we apply the Monte Carlo Tree Search model (MCTS for short) to simulate the process of recommending POI sequence in the dynamic environment, i.e., the weather and time change after visiting a POI. What’s more, we consider different speeds to reflect the fact that people may take different transportation to transfer between POIs. To validate the efficacy of DPSR , we conduct extensive experiments. The results show that our model can improve the accuracy of the recommendation significantly. Furthermore, it can better meet user preferences and enhance experiences.

A Serendipity-Oriented Personalized Trip Recommendation Model

Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip.

Recommending Reforming Trip to a Group of Users

With the quick evolution of mobile apps and trip guidance technologies, a trip recommender that recommends sequential points of interest (POIs) to travelers has emerged and recently received popularity. Compared to other outing recommenders, which suggest the following single POI, our proposed trip proposal research centers around the POI sequence proposal. An advanced sequence of the POI recommendation system named Recommending Reforming Trip (RRT) is presented, recommending a dynamic sequence of POIs to a group of users. It displays the information progression in a verifiable direction, and the output produced is the arrangement of POIs to be expected for a group of users. A successful plan is executed depending upon the deep neural network (DNN) to take care of this sequence-to-sequence problem. From start to finish of the work process, RRT can permit the input to change over time by smoothly recommending a dynamic sequence of POIs. Moreover, two advanced new estimations, adjusted precision (AP) and sequence-mindful precision (SMP), are introduced to analyze the recommended precision of a sequence of POIs. It considers the POIs’ consistency and also meets the sequence of order. We evaluate our algorithm using users’ travel histories extracted from a Weeplaces dataset. We argue that our algorithm outperforms various benchmarks by satisfying user interests in the trips.

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches.

HiRecS: A Hierarchical Contextual Location Recommendation System

The point-of-interest (POI) recommender exploits check-in information from location-based social networks (LBSNs) to recommend POIs that match user preferences. The preferences of users vary with region/locality, consumption type, and co-consumers. For instance, the places preferred for friends may be different from the places preferred for family, and the places preferred in one locality may be different from the places in another locality. These dynamic preferences can be crucial for an efficient recommender system. A locality consists of different preference trends, such as “known for food and recreational sites.” In real-world, different sets of users might be attracted toward different preference trends, and some users’ preferences might overlap across multiple preference sets. Hence, an efficient aggregation of locality preferences is essential for the recommender systems. Many existing studies simply group items by their category and use simple collaborative filtering (CF) for recommendations, however, such techniques cannot efficiently handle the aggregated locality preferences. We propose a hierarchical recommendation model termed Hi erarchical Contextual Location Rec ommendation S ystem (HiRecS) that formulates users’ preferences as a hierarchical structure and models the locality trend using aggregated hierarchy. For a locality, the root of hierarchy contains preferred $k$ items from a set of visitors, and the subsequent levels contain preference wise subsets of those items. We also present a hierarchy aggregation technique to aggregate the hierarchical preferences from a similar set of users. The aggregated hierarchy is then contextually exploited for POI sequence recommendation. The core contributions of this article are: 1) it formulates the locality trends as hierarchical structures, presents a hierarchy aggregation technique, and models the personalized POI preferences with the aggregated hierarchy; 2) it exploits the aggregated popular trends to generate contextual POI sequence recommendation; and 3) it extensively evaluates the proposed model with two real-world data sets and demonstrates a significant performance gain of 0.006–5.91 on diversity metrics, 0.0349–17.51 on displacement metrics, and 0.114–0.289 on normalized discounted cumulative gain (NDCG) metrics, when compared to several baselines and relevant studies.

Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.

Context-Aware POI Sequence Recommendation with Attention-Based Neural Network

Abstract. Location-based social networks (LBSNs) is playing an increasingly important role in our daily life, through which users can share their locations and location-related contents at any time. The Location information implicitly expresses user's behaviour preference. Therefore, LBSNs is being widely explored for Point-of-Interest (POI) recommendation in recent years. Most of existing POI recommenders only recommend a single POI, while sometimes successive POI sequence recommendation is more practical. For example, when we travel to a strange city, what we expect is not a single POI recommendation, but a POI sequence recommendation which contains a set of POIs and the order of visiting them. To solve this problem, this paper proposes a novel model called Context-Aware POI Sequence Recommendation (CPSR), which is developed based on an attention-based neural network. Neural network has made a great success in various of field because of its powerful learning ability. Recently, dozens of works has demonstrated that attention mechanism can make the neural network models more reasonable.