Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity, i.e., misrepresenting universal relevance as causality, may also hinder us from understanding users’ travel decisions. To this end, in this article, we propose a factor-level causal explanation generation framework based on counterfactual data augmentation for user travel decisions, named Factor-level Causal Explanation for User Travel Decisions (FCE-UTD), which can distinguish between true and false causal factors and generate true causal explanations. Specifically, we first assume that a user decision is composed of a set of several different factors. Then, by preserving the user decision structure with a joint counterfactual contrastive learning paradigm, we learn the representation of factors and detect the relevant factors. Next, we further identify true causal factors by constructing counterfactual decisions with a counterfactual representation generator, in particular, it can not only augment the dataset and mitigate the sparsity but also contribute to clarifying the causal factors from other false causal factors that may cause spurious explanations. Besides, a causal dependency learner is proposed to identify causal factors for each decision by learning causal dependency scores. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our approach in terms of check-in rate, fidelity, and downstream tasks under different behavior scenarios. The extra case studies also demonstrate the ability of FCE-UTD to generate causal explanations in POI choosing.

Learning and extracting high-level features from point cloud is the key to improving the segmentation performances on point clouds for many networks. At present, many networks present very deep structures to extract high-level features for 3D perception. However, we argue that even better results can be achieved by (i) building feature vectors that integrates multi-scale geometric features, and (ii) exerting discriminative constraints on the learning of mid-levels features. In this paper, we propose a Multi-scale Neighborhood Feature Extraction and Aggregation Model (MNFEAM) to enhance feature extraction for point cloud learning. We try to first extract multi-scale neighborhood information for each input point and then aggregate local information of a mid-level locality feature space, and finally integrate the aggregated local and the global feature vectors. A new discriminative loss function is designed to strengthen the coarse semantics on mid-levels features so that the semantic abstraction process can be improved and accelerated. We improve the performances of three popular networks for point cloud segmentation using the proposed MNFEAM on standard 3D datasets.

Nowadays the application of visualization technology is very wide while big data technology is rapidly developing. Data visualization can show the results of data analysis in an intuitive way, which has a very important practical significance. This paper studies a variety of media data visualization methods along with the development of media information. First of all, we introduce main basic contents and methods of text visualization; secondly, we introduce micro-blog data visualization capabilities and applications under the impetus of text visualization development, and discuss its characteristics and social significance; finally, according to the micro-blog users forwarding hotspots public opinion, we introduce the geographic information data, which is able to be more in-depth data analysis and demonstrate the nature of the law of the things by visualization.

Information acquisition is an important part in the competitive intelligence system. With the development of network technology, network information has become an important information source of competitive intelligence acquisition. However, the complex and chaotic information on the Internet result in inefficiency of artificial collecting and processing intelligence. This paper has discussed the use of Delphi, database technology to design a network competitive intelligence acquisition system, and then attempted to implement downloading the text information automatically from a specific Web site and save them to the local computer database. The innovation of this system is to use monitoring tools to capture concerned information (such as information about competitors) in real-time, then pre-process and save them to the local computer database in structured form. This study not only has a practical significance on the collecting of business information and processing of competitive intelligence, but also provides a great deal of materials for future text mining and early warning.