Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.

KRAN: Knowledge Refining Attention Network for Recommendation

Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to Pareto’s principle, the important neighbors only account for a small proportion. These traditional algorithms can not fully mine the useful information in the KG. To fully release the power of KGs for building recommender systems, we propose in this article KRAN, a Knowledge Refining Attention Network, which can subtly capture the characteristics of the KG and thus boost recommendation performance. We first introduce a traditional attention mechanism into the KG processing, making the knowledge extraction more targeted, and then propose a refining mechanism to improve the traditional attention mechanism to extract the knowledge in the KG more effectively. More precisely, KRAN is designed to use our proposed knowledge-refining attention mechanism to aggregate and obtain the representations of the entities (both attributes and items) in the KG. Our knowledge-refining attention mechanism first measures the relevance between an entity and it’s neighbors in the KG by attention coefficients, and then further refines the attention coefficients using a “richer-get-richer” principle, in order to focus on highly relevant neighbors while eliminating less relevant neighbors for noise reduction. In addition, for the item cold start problem, we propose KRAN-CD, a variant of KRAN, which further incorporates pre-trained KG embeddings to handle cold start items. Experiments show that KRAN and KRAN-CD consistently outperform state-of-the-art baselines across different settings.

Addressing Cold Start Problem in Collaborative Filtering using Demographic Data with Entropy-based Methodology

Collaborative filtering based recommender systems (RS) are faced with cold start problem. This problem arises when the RS does not have enough information or opinion about a person or about a product and therefore cannot make recommendation for such person. In this paper, the demographic data of the user such as age, gender, and occupation are utilized as additional sources together with existing users’ rating to tackle the cold start problem by employing the entropy-based methodology to determine the degree of predictability. Experimental results on MovieLens dataset showed that the proposed method gives higher accuracy than other existing demographic based methods. Keywords— Cold Start, Collaborative Filtering, Entropy, Demographic Approach, Recommender Systems

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user.

Analysis of the NOx storage behaviour during cold start of modern SCR flow-through substrates and SCR on-filter substrates

Selective catalytic reduction (SCR) systems are the state-of-the-art technology to reduce nitrogen oxide emissions (NOx) of modern diesel engines. The system behaviour is well understood in the common temperature working area. However, the system properties below light-off temperature are less well known and offer a wide scope for further investigations. Vehicle measurements show that under specific conditions during cold start, NOx can be partially stored and converted on on-filter and flow-through SCR catalysts. The purpose of this work was in a first step to analyse the main influence parameters on the NOx storage behaviour. Therefore, synthetic gas test bench measurements have been carried out, varying the gas concentrations, temperature, and gas hourly space velocity (GHSV). These investigations showed that the NOx storage effect strongly depends on the NH3 level stored in the catalyst, GHSV, the adsorbed water (H2O) on the catalyst, and the temperature of the catalyst. Further influence parameters such as the gas composition with focus on carbon monoxide (CO), short-chain hydrocarbons and long-chain hydrocarbons have been analysed on a synthetic gas test bench. Depending on operating conditions, a significant amount of NOx can be stored on a dry catalyst during the cold start phase. The water vapor from the combustion condenses on the cold exhaust pipe during the first seconds, or up to a few minutes after a cold start. As the water vapor reaches the surface of the catalyst, it condenses and adsorbs onto it, leading to a sudden temperature rise. This exothermal reaction causes the stored NOx to be desorbed, and furthermore it is partially reduced by the NH3 stored in the catalyst.