Review-based recommendation algorithms can alleviate the data sparsity issue in collaborative filtering by combining user ratings and reviews in model learning. However, most existing methods simplify the feature extraction process from reviews by assuming that different granularities of information (e.g., word, review, and feature) are equally important, which cannot optimally leverage the most important information and thus achieves suboptimal recommendation accuracy. Besides, many existing works directly regard text features as users or items representations, which may not be enough to make precise representations due to the large amount of redundant information in reviews. To tackle the two problems mentioned above, we propose a deep learning-based method named Hierarchical Attention Network Oriented Towards Crowd Intelligence (HANCI). First, HANCI replaces the commonly-used topic models or CNN text processor with an RNN text processor in review feature extraction, which can fully exploit the advantages of the sequential dependencies of reviews by using the whole hidden layers of the bidirectional LSTM as outputs. Second, HANCI weighs the importance of features guided by crowd intelligence to more accurately represent each user on each item, and vice versa. Third, HANCI utilizes a hierarchical attention network based on multi-level review text analysis to extract more precise user preferences and item latent features, so that HANCI can explore the importance of words, the usefulness of reviews and the importance of features to achieve more accurate recommendation. Extensive experiments on three public datasets show that HANCI outperforms the state-of-the-art review-based recommendation algorithms in accuracy and meanwhile provides insightful explanations.

Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence

In this paper, we propose a Geographic Information Mining framework to contribute some exploratory results concerning harvesting the featured place information entities from the Web. In the framework, we suggest an iterative geographic information mining model reflecting the data evolution along the mining process. Associating the iterations, we propose a set of methodologies and integrate them into the processing onto solving the critical issues concerning collecting data, filtering irrelevant samples and extracting featured entities. According to the experiments, the contribution brings in a sound systematic solution to enrich the existing digital gazetteers as complete as Google Maps.

Extracting geographic features from the Internet: A geographic information mining framework

This article presents an intelligent experience retention system (IERS), which is designed to overcome challenges and limitations of capturing human experience related to operating procedures for plant operation and maintenance in nuclear power plants. It is time-consuming to find specific information from thousands of input documents. Less experienced employees cannot operate complex tasks due to having less knowledge and training about the documents and their operation. Research gaps in current knowledge structuring and retrieval methods are discussed and used to identify essential features to achieve effective methods to manage instructive text (iText) related to learning and answering queries connected to operating procedures. Knowledge structure is proposed to represent inputs from documents, data, text, and voice related to operation and maintenance instructions in nuclear power plants. Human experience is captured and integrated within the structured knowledge in an integrated scheme, called the human experience semantic network (HESN), which includes deterministic, qualitative, and probabilistic parameters and attributes that are captured and dynamically tuned throughout the execution of the system.

An Intelligent Experience Retention System: Challenges and Limitations for Operation and Maintenance in Nuclear Power Plants

Concept lattice theory, which is one of the key mathematical models of granular computing, is capable of successfully dealing with uncertain information in nominal data. It has been applied to machine learning tasks such as data reduction, classification, and association rule mining. For the problem of outlier detection in nominal data, this paper presents a concept lattice theory-based approach for detecting outliers in nominal data. First, subcontexts and concept lattices based on subsets of objects are discussed. Then, information entropy is introduced into the formal context, and an object entropy based on attribute granules is proposed. Finally, a nominal data-oriented outlier detection method is explored based on the proposed object entropy. The experimental results show that the proposed detection method can effectively detect outliers in nominal data. Besides, the results of the hypothesis testing indicate that the proposed method is statistically significantly different from the other methods. The code is publicly available online at https://github.com/from-china-to/OEOD. 

Attribute granules-based object entropy for outlier detection in nominal data

Protecting enterprise information security is a main task of Internet of Things system. The interaction between employees in same enterprise is based on level structures. So it is important to discover levels of employees for urban developers to protect enterprise information security. In this article, we propose a level discovery method for employees (LDME) from the records of employees using mobile phones named LDME. The call behavior between employees are expressed as several weighted directed complex networks, LDME represent edges in these weighted directed complex networks as vectors to exact both direction and weight information of the edges. Combined with supervised learning method, LDME prune these weighted directed networks into directed acyclic networks, which accurately reflect levels information between employees. At the same time, LDME mines the maximal frequent directed acyclic substructure from the above directed acyclic networks with efficient way, which indicate the stable levels information. We use real data to verify the performance of our method. The experimental result shows that the level of employees mined with our method is accurate and stable.

AI and Machine Learning for Industrial Security With Level Discovery Method

Nowadays, as is envisioned as one of the most popular and challenging research areas due to the rapid growth of web data, information retrieval (IR) serves as a fundamental technology in large scale dataset processing and analyzing. IR systems usually involve handling massive and continuous retrieval requests in data matching, information filtering and other application scenarios. However, in the general applications of IR such as search engines, the individual response time is mostly emphasized and the efficiency of handling massive queries mainly relies on caching or similar technologies. For improving the overall efficiency of handling massive queries, we design a batched information retrieval system which first analyzes a batch of queries and then utilizes the repeats, similarity and correlations among queries to accelerate the retrievals. A concept-lattice-like structure called keyword-DAG (Directed Acyclic Graph) is first exploited to store and organize the similarity among queries. Accordingly a keyword-DAG processing algorithm namely pruning is devised to implement the batched retrieval. Then an incremental ranking algorithm is presented for the batched IR scenarios, which has be demonstrated (both in theory and practice) to be able to remarkably shorten the retrieval time. Finally, an overall planning algorithm is proposed for choosing the optimal pruning path and improving the utilization of memory. The experiment results show that our approach embraces far better performance compared with the traditional separate retrieval method in mass data processing and analyzing scenarios.

Design a batched information retrieval system based on a concept-lattice-like structure

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Design a batched information retrieval system based on a concept-lattice-like structure