基于用户-项目交叉注意力机制的迁移推荐模型

随着互联网技术的发展,个性化的推荐系统得到了广泛应用.但用户数据稀疏与冷启动仍是推荐系统普遍面临的难题.将深度学习与注意力机制相结合,提出基于用户-项目交叉注意力机制的迁移推荐模型.该模型能够充分学习源域数据中用户、物品及评分间的潜在关系,然后初始化目标域神经网络,迁移应用到目标域.为验证算法模型的有效性,在公开数据集...     

Research on recommendation and interaction strategies based on resource similarity in the manufacturing ecosystem

Nowadays, the thriving of the manufacturing ecosystems (ME) driven by the increasing competition in industrial markets, the ubiquitous implementation of intelligent systems, and the more frequent collaboration among manufacturing enterprises. During the practice of the system upgrade, it is increasingly noted that the redundancy of manufacturing resources and the inefficiency in resource configuration are the major obstacles to achieving satisfying value-creation within ME, which also result in cumbersome decision making (DM) in the problems of requirement-service configuration (RSC) and collaborative production. To address these issues, the research on resource recommendation and interaction is carried out. Firstly, the resource similarity models for autonomous resource filtering brace the whole DM mechanism in RSC and push the most suitable resource to the host automatically. Then, the interaction model provides a self-organized production mode without human intervention. The blindness, lag, and unfairness in the manual communication is eliminated by the Machine to Machine (M2M) interaction and automatic coordination. Besides, an NLP-based machine learning algorithm is introduced for quantifying semantic distance and measuring the differences between orders. Composed by these models, a total solution named Industry-Chat (I-Chat) emerges. With the help of that, production resources can be scheduled and managed autonomously and the order-based production processes could be promoted seamlessly. Thus, an improved industrial ecosystem with automatic DM and self-organization for future intelligent manufacturing is realized. The practicability of the research is verified by a case study. The results show that the production cost is reduced by 12%, the resource utilization rate is improved and its economic value is demonstrated.     

基于覆盖约简的个性化协同过滤推荐方法

协同过滤(CF)无法同时提供高精度和多样化的个性化推荐.基于此情况,文中提出基于覆盖约简的协同过滤方法(CRCF).结合覆盖粗糙集中的覆盖约简算法与CF中的用户约简,匹配覆盖中的冗余元素与邻近用户中的冗余用户,利用覆盖约简算法将冗余用户从目标用户的邻近用户中移除,保证CF中邻近用户的高效性.在公开数据集上的实验表明,在稀疏数据环境下,CRCF可以同时为目标用户提供高精度和多样化的个性化推荐.     

A Novel Fuzzy Rough Sets Theory Based CF Recommendation System

Collaborative Filtering (CF) is one of the popular methodology in recommender systems. It suffers from the data sparsity problem, recommendation inaccuracy and big-error in predictions. In this paper, the efficient advisory tool is implemented for the younger generation to choose their right career based on their knowledge. It acquires the notions of indiscernible relation from Fuzzy Rough Sets Theory (FRST) and propose a novel algorithm named as Fuzzy Rough Set Theory Based Collaborative Filtering Algorithm (FRSTBCF). To evaluate the model, data is prepared using the cross validation method. Based on that, ratings are evaluated by calculating the MAE (mean average error), MSE (means squared error) and RMSE (root means squared error) values. Further the correctness of the model is measured by finding rates like Accuracy, Specificity, Sensitivity, Precision & False Positive Rate. The proposed FRSTBCF algorithm is compared with the traditional algorithms experiment results such as Item Based Collaborative Filtering using the cosine similarity (IBCF-COS), IBCF using the pearson correlation (IBCF-COR), IBCF using the Jaccard similarity (IBCF-JAC) and Singular Value Decomposition approximation (SVD). The proposed algorithm gives better error rate and its precision value is comparatively identical with the existing system.     

Target and demand for renewable energy across 10 ASEAN countries by 2040

The member countries of Southeast Asia have renewable energy sources that never run out like solar energy, wind, geothermal, hydropower, biomass, etc. Geographically, Southeast Asian countries are between two continents that have tropical and humid climate conditions in general, while only Myanmar has a subtropical climate because it is astronomically. This provides easier access to many renewable energy sources. The governments of ASEAN member countries have made several policies and promoted renewable energy to encourage individuals and industries to use renewable energy in the future. This study provides information on the status of renewable energy as a comprehensive substitute for fossils in Southeast Asian countries, which includes the potential for renewable energy in the region and the capacity of renewable energy currently available. The study also provides brief information on the potential of renewable energy, renewable energy targets, and challenges to energy demand in Southeast Asia. In addition, this paper provides several recommendations for renewable energy in Southeast Asian countries.     

Recommendation Algorithm Based on Probabilistic Matrix Factorization with Adaboost

A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements, taking into account individual characteristics, such as body weight with individual health conditions, such as diabetes. Current dietary recommendations employ association rules, content-based collaborative filtering, and constraint-based methods, which have several limitations. These limitations are due to the existence of a special user group and an imbalance of non-simple attributes. Making use of traditional dietary recommendation algorithm researches, we combine the Adaboost classifier with probabilistic matrix factorization. We present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost. A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional characteristics. From this, we can make use of those features with strong influence while discarding those with little influence. After incorporating these changes into our approach, we evaluated our algorithm’s performance. Our results show that our method performed better than others at matching preferred foods with dietary requirements, benefiting user health as a result. The algorithm fully considers the constraint relationship between users’ attributes and nutritional characteristics of foods. Considering many complex factors in our algorithm, the recommended food result set meets both health standards and users’ dietary preferences. A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability.     

一种新的经典文献推荐机制的设计

经典文献对于初学者进入某一新的研究领域大有帮助,然而,如何发掘出这些经典文献是一个难题。设计一种新的经典文献推荐机制,这一机制基于两条规律:下载持续律和引用传递律。下载持续律指出经典文献的下载频率不会因为其发表时间距今久远而有所降低;引用传递律指出如果某经典文献被引用,则它引用的那些文献也很可能被引用。首先用实验证明上述两条规律,然后进一步用真实数据来挖掘出特定领域的经典文献。事实证明该设计的机制所推荐的经典文献有着较高的质量和准确性。     

融合标签信息的卷积矩阵分解推荐算法

当今各类推荐系统中存在着冷启动、数据稀疏性的问题,严重影响其推荐质量。为了有效缓解由于数据不完整导致的推荐效果不理想,提出一种融合标签信息的卷积矩阵分解推荐算法TaSoConvMF(Convolutional Matrix factorization Recommendation Algorithm Fusing Social Tagging)。该算法将卷积神经网络融合进概率矩阵分解模型,并利用评分矩阵和标签矩阵联合监督,运用联合概率矩阵分解计算用户-资源、用户-标签、资源-标签三个矩阵的隐式向量,根据评分矩阵多次对模型参数进行优化。该算法通过在豆瓣评分数据集和MovieLens10M数据集上进行多次实验,采用RMSE指标进行评估,预测结果表明推荐效果有所提升。     

On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm

Recommendation-aware Content Caching (RCC) at the edge enables a significant reduction of the network latency and the backhaul load, thereby invigorating ubiquitous latency-sensitive innovative services. However, the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’ content request patterns, the sophisticated caching placement policy, and the personalized recommendation tactics. In this article, we investigate how the potentials of Artificial Intelligence (AI) and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era. Towards this end, we first elaborate on the hierarchical RCC network architecture. Then, the devised AI and optimization empowered paradigm is introduced, whereas AI and optimization techniques are leveraged to predict the users’ content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision, respectively. Through extensive case studies, we validate the effectiveness of AI-based predictors in estimating users’ content preference and the superiority of optimized RCC policies over the conventional benchmarks. At last, we shed light on the opportunities and challenges in the future.     

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review. The collected information is analyzed and processed by batch normalized capsule networks (NCN). The network explores the user reviews according to product details, time, price purchasing factors, etc., ensuring product quality and ratings. Then effective recommendation system is developed using a butterfly optimized matrix factorization filtering approach. Then the system’s efficiency is evaluated using the Rand Index, Dunn index, accuracy, and error rate.