Abstract The next Point of Interest (POI) recommendation is the core technology of smart city. Current state-of-the-art models attempt to improve the accuracy of the next POI recommendation by incorporating temporal and spatial intervals or by partitioning the POI coordinates into grids. However, they all overlook a detail that in real life, people always want to know where to go at an exact time point or after a specific time interval instead of aimlessly asking where to go next. Moreover, due to individual preferences, different users may visit different places at the same timestamp. Therefore, utilizing timestamp queries can enhance the personalized recommendation capability of the model and mitigate overfitting risks. These implies that using timestamp can achieve more precise recommendations. To the best of our knowledge, we are the first to use the next timestamp for next POI recommendation. In particular, we propose a Time-Stamp Cross Attention Network (TSCAN). TSCAN is a two-layer cross-attention network. The first layer, Time Stamp Cross Attention Block (TSCAB), uses cross-attention between the next timestamp and historical timestamps, and multiplies the attention scores on corresponding POI to predict the next POI that is most related to the history. The other layer, Cross Time Interval Aware Block (CTIAB), applies the time intervals between the next timestamp and historical timestamps to the POI obtained by TSCAB and historical POIs, allowing temporally adjacent POIs to have a greater similarity. Our model not only has a significant improvement in accuracy but also achieves the goal of personalized recommendation, effectively alleviating overfitting. We evaluate the proposed model with three real-world LBSN datasets, and show that TSCAN outperforms the state-of-the-art next POI recommendation models by 5~9%. TSCAN can not only recommend the next POI, but also recommend the possible POI to visit at any specific timestamp in the future.

Graph convolutional network (GCN) has rapidly developed in various fields due to its powerful modeling capability. However, most of the researches directly inherit the complex design of GCN, such as feature transformation and nonlinear activation, which lacks thorough ablation analysis on GCN. In addition, implicit feedback is not fully utilized and data sparsity is not well resolved, which are also shortcomings of current recommendation algorithms. To solve the above problems, this paper proposes a lightweight graph convolutional collaborative filtering (F-LightGCCF) recommendation approach incorporating social relationships. Firstly, it abandons the design of feature transformation and nonlinear activation in graph convolutional models and simplifies model training. Additionally, a series of intermediate feedback from users’ implicit negative feedback is generated by taking advantage of social networks, which improves the utilization of implicit negative feedback. Secondly, it can model the long-range dependencies between users and items by using the dual attention mechanism, aggregating the contribution values of neighboring nodes and the importance of the learning vectors in each layer of the graph convolution layer respectively. Lastly, the inner product operation is used to obtain the association score between users and items. Extensive experiment results on two real-world datasets show that F-LightGCCF outperforms existing state-of-the-art recommendation methods. Further ablation studies and analyses validate the efficiency and effectiveness of the F-LightGCCF model.

Abstract Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented.

Due to the widespread uses of uncertain spatiotemporal data, web ordinary users have access to query these data in various ways. However, users often cannot accurately give query constraints so that the query results may be empty or very few. Traditional algorithms cannot be used to deal with uncertain spatiotemporal data because they have no relaxation query on spatiotemporal attributes. Therefore, in this paper, the authors propose new flexible query algorithms, which add relaxation query processing for spatiotemporal attributes. Considering that XML has great advantages in exchanging and representing spatiotemporal data, they propose an uncertain spatiotemporal data model based on XML. According to the different number of relaxing attributes, they give SingleRelaxation algorithm and MultipleRelaxation algorithm. In addition, a T-List structure is designed to quickly locate the nodes' positions of uncertain spatiotemporal data, and RSort algorithm is proposed to sort accurate query results and extended query results. The experimental results show the superiority of the approach.

Diversity point-of-interest recommendation system benefits users to broaden their interests, access and discover new interest points. This paper describes a Diversified and Personalized Point-Of-Interest Recommendation System (DP-POIRS) by leveraging the geo-social relationships between POIs. The system consists of three components. The first component - geo-social distance measuring component is used to construct a correlation matrix to describe the geo-social distance between points of interests. The second component -point-of-interest partition component, divides the interest points into diverse clusters by using the spectral clustering algorithm over the correlation matrix. The third component -personalized sorting component, finds out the user's favorite interest points from each cluster, and then sorts them into a list of recommendation by the use of matrix factorization algorithms.

Spatial database queries are often exploratory. The users often find that their queries return too many answers and many of them may be irrelevant. Based on the coupling relationships between spatial objects, this paper proposes a novel categorization approach which consists of two steps. The first step analyzes the spatial object coupling relationship by considering the location proximity and semantic similarity between spatial objects, and then a set of clusters over the spatial objects can be generated, where each cluster represents one type of user need. When a user issues a spatial query, the second step presents to the user a category tree which is generated by using modified C4.5 decision tree algorithm over the clusters such that the user can easily select the subset of query results matching his/her needs by exploring the labels assigned on intermediate nodes of the tree. The experiments demonstrate that our spatial object clustering method can efficiently capture both the semantic and location correlations between spatial objects. The effectiveness and efficiency of the categorization algorithm is also demonstrated.

Due to imprecise query intention, Web database users often use a limited number of keywords that are not directly related to their precise query to search information. Semantic approximate keyword query is challenging but helpful for specifying such query intent and providing more relevant answers. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query approach which generates semantic approximate answers by identifying a set of keyword queries from the query history whose semantics are related to the given keyword query. To capture the semantic relationships between keywords, a semantic coupling relationship analysis model is introduced to model both the intra- and inter-keyword couplings. Building on the coupling relationships between keywords, the semantic similarity of different keyword queries is then measured by a semantic matrix. The representative queries in query history are identified and then a priori order of remaining queries corresponding to each representative query in an off-line preprocessing step is created. These representative queries and associated orders are then used to expeditiously generate top-k ranked semantically related keyword queries. We demonstrate that our coupling relationship analysis model can accurately capture the semantic relationships both between keywords and queries. The efficiency of top-k keyword query selection algorithm is also demonstrated.

Due to the insufficient knowledge of users about the database schema and content, most of them cannot easy to find appropriate keywords to express their query intentions. This paper proposes a novel approach, which can provide a list of keywords that semantically related to the set of given query keywords by analyzing the correlations between terms in database and query keywords. The suggestion would broaden the knowledge of users and help them to formulate more efficient keyword queries. To capture the correlations between terms in database and query keywords, a coupling relationship measuring method is proposed to model both the term intra- and intercouplings, which can reveal the explicit and implicit relationships between terms. For a given keyword query, based on the coupling relationships between terms, an order of terms in database is created for each query keyword and then the threshold algorithm (TA) is to expeditiously generate top-k ranked semantically related terms. The experiments demonstrate that our term coupling relationship measuring method can efficiently capture the semantic correlations between terms. The performance of top-k related term selection algorithm is also demonstrated.

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, the fuzzy UML model is investigated in detail, and a kind of formal definition of fuzzy UML models is proposed. Then, a kind of fuzzy ontology called fuzzy OWL DL ontology is introduced. Furthermore, we consider the fuzzy UML model and the corresponding fuzzy UML instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. In addition, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy UML models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-SHOIN(D) is investigated, which can help to construct fuzzy ontologies more exactly.

To deal with the problem of empty or too little answers returned from a Web database in response to a user query, this paper proposes a novel approach to provide relevant and ranked query results. Based on the user original query, we speculate how much the user cares about each specified attribute and assign a corresponding weight to it. This original query is then rewritten as an approximate query by relaxing the query criteria range. The relaxation order of all specified attributes and the relaxed degree on each specified attribute are varied with the attribute weights. For the approximate query results, we generate users' contextual preferences from database workload and use them to create a priori orders of tuples in an off-line preprocessing step. Only a few representative orders are saved, each corresponding to a set of contexts. Then, these orders and associated contexts are used at query time to expeditiously provide ranked answers. Results of a preliminary user study demonstrate that our query relaxation and results ranking methods can capture the user's preferences effectively. The efficiency and effectiveness of our approach is also demonstrated by experimental result.

To deal with the problem of too many results returned from a Web database in response to a user query, this paper proposes a novel approach, which takes advantage of the contextual preferences to precompute a few representative orders of tuples and uses them to expeditiously provide ranked answers factoring in the information contained in the query. Contextual preferences take the form that item i1 is preferred to item i2 with an interest degree in the context of X. This paper formally defines contextual preferences, provides algorithms for creating tuple orders, clustering orders and processing queries, and presents experimental results to show their efficiency.