Knowledge graph completion (KGC) can predict missing links and is crucial for real-world knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Recently, emerging methods have shown improved predictive results by further incorporating the timestamps of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods can learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the problem definition, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how timestamps of facts are used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.

Temporal Knowledge Graph Completion: A Survey

GraIL and its variants have shown their promising capacities for inductive relation reasoning on knowledge graphs. However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs. Besides, the enclosing subgraph extraction in most GraIL-based models restricts the model from extracting enough discriminative information for reasoning. Consequently, the expressive ability of these models is limited. To address the problems, we propose a novel GraIL-based framework, termed MINES, by introducing a Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph. Concretely, the message intercommunication mechanism is designed to capture the omitted hidden mutual information. It introduces bi-directed information interactions between connected entities by inserting an undirected/bi-directed GCN layer between uni-directed RGCN layers. Moreover, inspired by the success of involving more neighbors in other graph-based tasks, we extend the neighborhood area beyond the enclosing subgraph to enhance the information collection for inductive relation reasoning. Extensive experiments prove the promising capacity of the proposed MINES from various aspects, especially for the superiority, effectiveness, and transfer ability. 

MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs

Recently, POI (Point-of-interest) recommendation for groups has become a critical challenge when helping groups to discover potentially interesting new places, and some effective recommendation models have been proposed to address this issue. However, most existing research focuses on POI recommendation for fixed groups, few studies have been conducted on POI recommendation for occasional groups. To tackle this issue, we propose a POI recommendation model for occasional groups based on Hybrid Graph Neural Networks (termed as PROG-HGNN) which combines excellent graph neural networks models. Firstly, PROG-HGNN generates the fitted representation of the occasional group based on the Node Influence Indicator (INF) method and Graph Attention Networks (GAT) model. Then, PROG-HGNN learns POIs’ representations containing members’ POI interaction preferences and members’ POI transfer preferences with the Signed Bipartite Graph Neural Networks (SBGNN) model and the Session-based Graph Neural Networks (SRGNN) model, respectively. Finally, PROG-HGNN recommends the potential POIs for the occasional group based on the fitted representation of the occasional group and the learned representations of POIs. We verify our proposed model on three public benchmark datasets (Foursquare, Gowalla and Yelp), which contains 124,933 to 860,888 POI check-in records. The comparison between our proposed model and the twelve baseline models demonstrates the outstanding performance of PROG-HGNN. In terms of Precision@K and Recall@K, our model achieves about 32.92% and 19.67% improvement compared with the best baseline models on the three benchmark datasets averagely. Adequate ablation experiments prove the effectiveness of the members’ POI interaction preferences learning module and POI transfer preferences learning module. 

POI recommendation for occasional groups Based on hybrid graph neural networks

The next point-of-interest (POI) recommendation task recommends users POIs that they may be interested in based on their historical trajectories. This task holds value for users as well as service providers; however, it is difficult. Although users exhibit repetitive and periodic behavioral characteristics at the macro level, such characteristics are influenced by individual preferences and diverse factors at the micro level, rendering prediction difficult. Most existing sequence modeling methods consider intervals between elements to be invariants. However, the time and distance intervals between adjacent POIs in the user's check-in sequences are irregular, which contains significant user behavioral characteristics. Therefore, we propose a model known as Irregular Interval Attention and Multi-task Learning for Next POI Recommendation (ImNext). First, to address data sparsity and irregular intervals in the check-in sequence, we designed a data augmentation method to improve data density and proposed a novel irregular interval attention (IrrAttention) module. Second, to deal with the potential factors that affect user behavior, we proposed a graph attention network module that integrates edge attention (EA-GAT), which incorporates edge weights in the user's spatiotemporal and social transition graphs. Lastly, we established multiple subtasks for joint learning as the user's next check-in hides multiple targets, such as time and distance intervals. The experimental results show that our proposed method outperforms the state-of-the-art (SOTA) methods on two real-world public datasets. The implementation of the ImNext model is available at https://github.com/simplehx/ImNext. 

ImNext: Irregular Interval Attention and Multi-Task Learning for Next POI Recommendation

In location-based social networks, calibrating a point-of-interest (POI) recommendation system is as important as its accuracy for improving user satisfaction. POI recommendation calibration is primarily classified as categorical or geographical calibration. Categorical calibration ensures that the recommended items are distributed proportionally among the past interest categories of the target user. When a target user checks 80 Chinese, 10 Japanese, and 10 French restaurants, a recommendation list with a ratio of 8:1:1 for Chinese, Japanese, and French restaurants can be reasonably expected. In addition to categorical calibration, geographical calibration has been proposed to increase user interest in the recommended results. Users have a high probability of revisiting locations in their subareas. Therefore, the POIs recommended in multiple subareas of interest are more suitable than those from one small and frequently visited subarea. However, improving the calibration and accuracy are conflicting tasks. To achieve high calibration while maintaining accuracy, previous studies proposed reranking-based techniques to rerank the candidate list and return POIs with high calibration. However, optimizing the calibration by reranking is independent of the basic-candidate-item generation model, resulting in a suboptimal system. To tackle the problem, we propose a novel sampling-based differentiation technique to merge the task of improving calibration into the GCN model training process and directly generate the final recommendation list. The model is flexible and can be applied to different domains, where a domain can be a subarea or category. In a three-layer GCN, the layer one represents the historical check-ins of the user, whereas layer three includes the candidate POIs from which the target user aggregates information. We trained the model to make the distribution of the POI domains at layer three approximated the distribution at layer one. Experimental results on Philadelphia and Tucson datasets confirmed that the proposed method outperforms all state-of-the-art GCN+ geo-reranking and GCN+ MCF baselines, improving Recall@ 5 from 0.0394 to 0.0412 (4.57%) and Jensen–Shannon measure (JS)@ 5 from 0.5931 to 0.6734 (13.54%) on the Philadelphia dataset and improving Recall@ 5 from 0.0495 to 0.0517 (4.40%) and JS@ 5 from 0.5869 to 0.6598 (12.42%) on the Tucson dataset for categorical calibration. The model was also tested in the geographical domain and a similar trend was observed. 

Sampling-based Epoch Differentiation Calibrated Graph Convolution Network for Point-of-interest Recommendation

Next Point-of-Interest (POI) recommendation has shown great value for both users and businesses in the field of location-based services. Many spatial–temporal inferring methods have been developed to perform this task, but the data sparsity of POI trajectories greatly hinders the recommendation performance. Knowledge graphs (KGs) have been demonstrated as an effective way to tackle data sparsity in the general recommendation field by leveraging the valuable information of entities and relations. Yet, few studies have explored applying KGs for the next POI recommendation task because of the following challenges: (1) how to represent the dynamic mobility behaviors of users with the static entities and relations in KGs; and (2) how to utilize the different types of entities and relations in KGs to capture long- and short-term preferences of users. In this work, we investigate building a spatial–temporal KG (STKG) from check-in sequences of users to promote the next POI recommendation, without introducing any external attributes of users and POIs. In STKG, we design a novel spatial–temporal transfer relation to intuitively capture users’ transition patterns between neighboring POIs. Then, based on the STKG, we propose an innovative model, named STKGRec, for the next POI recommendation, which explicitly models long- and short-term preferences of users in an end-to-end manner. In particular, STKGRec learns both the spatial–temporal correlation of consecutive and nonconsecutive visits in the current check-in sequence to comprehensively capture the short-term preferences of users. Extensive experiments on four real-world datasets demonstrate the superiority of STKGRec against the state-of-the-art baseline methods. The code of our proposed model is available at https://github.com/WeiChen3690/STKGRec . 

Building and exploiting spatial-temporal knowledge graph for next POI recommendation

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Building and exploiting spatial-temporal knowledge graph for next POI recommendation