Enhanced Collaborative Filtering for Personalized E-Government Recommendation

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms.

Smart Relocation System

With the increase of social mobility, the relocation of work and living places has become the norm in people’s daily life. However, the current level of informatization in the handling industry is generally low. If users want to know the progress of the porter age, they can only go to the site in person; after the porter age is completed, if they want to find the items, they need to search through the piles of boxes. It is even impossible to know the detailed location of the items. So there is an urgent need for an intelligent system that can comprehensively control the entire handling process from inventory, porter age to item positioning. The intelligent handling system designed in this paper uses the Internet of Things, network communication, artificial intelligence, cloud computing and other technologies, which can sensitively capture the packed items to improve the packing efficiency, quickly and accurately identify the name of the item to facilitate searching, and establish multi-level tags to store item information and achieve fuzzy matching, and encryption at each stage to ensure user privacy.

A direct neural measure of variable precision representations in visual working memory

Visual working memory (VWM) is an active representation enabling the manipulation of item information even in the absence of visual input. A common way to investigate VWM is to analyze the performance at later recall. This approach, however, leaves uncertainties about whether the variation of recall performance is attributable to item encoding and maintenance or to the testing of memorized information. Here, we record the contralateral delay activity (CDA) - an established electrophysiological measure of item storage and maintenance - in human subjects performing a delayed orientation precision estimation task. This allows us to link the fluctuation of recall precision directly to the process of item encoding and maintenance. We show that for two sequentially encoded orientation items, the CDA amplitude reflects the precision of orientation recall of both items, with higher precision being associated with a larger amplitude. Furthermore, we show that the CDA amplitude for each item varies independently from each other, suggesting that the precision of memory representations fluctuates independently.

A Dual-Attention Autoencoder Network for Efficient Recommendation System

Accelerated development of mobile networks and applications leads to the exponential expansion of resources, which causes problems such as trek and overload of information. One of the practical approaches to ease these problems is recommendation systems (RSs) that can provide individualized service. Video recommendation is one of the most critical recommendation services. However, achieving satisfactory recommendation service on the sparse data is difficult for video recommendation service. Moreover, the cold start problem further exacerbates the research challenge. Recent state-of-the-art works attempted to solve this problem by utilizing the user and item information from some other perspective. However, the significance of user and item information changes under different applications. This paper proposes an autoencoder model to improve recommendation efficiency by utilizing attribute information and implementing the proposed algorithm for video recommendation. In the proposed model, we first extract the user features and the video features by combining the user attribute and the video category information simultaneously. Then, we integrate the attention mechanism into the extracted features to generate the vital features. Finally, we incorporate the user and item potential factor to generate the probability matrix and defines the user-item rating matrix using the factorized probability matrix. Experimental results on two shared datasets demonstrates that the proposed model can effectively ameliorate video recommendation quality compared with the state-of-the-art methods.

An Efficient Q -Algorithm for RFID Tag Anticollision

In large-scale Internet of Things (IoT) applications, tags are attached to items, and users use a radiofrequency identification (RFID) reader to quickly identify tags and obtain the corresponding item information. Since multiple tags share the same channel to communicate with the reader, when they respond simultaneously, tag collision will occur, and the reader cannot successfully obtain the information from the tag. To cope with the tag collision problem, ultrahigh frequency (UHF) RFID standard EPC G1 Gen2 specifies an anticollision protocol to identify a large number of RFID tags in an efficient way. The Q -algorithm has attracted much more attention as the efficiency of an EPC C1 Gen2-based RFID system can be significantly improved by only a slight adjustment to the algorithm. In this paper, we propose a novel Q -algorithm for RFID tag identification, namely, HTEQ, which optimizes the time efficiency of an EPC C1 Gen2-based RFID system to the utmost limit. Extensive simulations verify that our proposed HTEQ is exceptionally expeditious compared to other algorithms, which promises it to be competitive in large-scale IoT environments.

Can activated long-term memory maintain serial order information?

The maintenance of serial order information is a core component of working memory (WM). Many theoretical models assume the existence of specific serial order mechanisms. Those are considered to be independent from the linguistic system supporting maintenance of item information. This is based on studies showing that psycholinguistic factors strongly affect the ability to maintain item information, while leaving order recall relatively unaffected. Recent language-based accounts suggest, however, that the linguistic system could provide mechanisms that are sufficient for serial order maintenance. A strong version of these accounts postulates serial order maintenance as emerging from the pattern of activation occurring in the linguistic system. In the present study, we tested this assumption via a computational modeling approach by implementing a purely activation-based architecture. We tested this architecture against several experiments involving the manipulation of semantic relatedness, a psycholinguistic variable that has been shown to interact with serial order processing in a complex manner. We show that this activation-based architecture struggles to account for interactions between semantic knowledge and serial order processing. This study fails to support activated long-term memory as an exclusive mechanism supporting serial order maintenance.

Can activated long-term memory maintain serial order information?

The maintenance of serial order information is a core component of Working Memory (WM). Many theoretical models assume the existence of specific serial order mechanisms. Those are considered to be independent from the linguistic system supporting maintenance of item information. This is based on studies showing that psycholinguistic factors strongly affect the ability to maintain item information, while leaving order recall relatively unaffected. Recent language-based accounts suggest however that the linguistic system could provide mechanisms that are sufficient for serial order maintenance. A strong version of these accounts postulates serial order maintenance as emerging from the pattern of activation occurring in the linguistic system. In the present study, we tested this assumption via a computational modeling approach, by implementing a purely activation-based architecture. We tested this architecture against several experiments involving the manipulation of semantic relatedness, a psycholinguistic variable that has been shown to interact with serial order processing in a complex manner. We show that this activation-based architecture struggles to account for interactions between semantic knowledge and serial order processing. This study fails to support activated long-term memory as an exclusive mechanism supporting serial order maintenance.

A New Collaborative Filtering Recommendation Method Based on Transductive SVM and Active Learning

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a “maximum-minimum segmentation” of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency.

Personalized Hybrid Educational Recommender System Using Matrix Factorization with User and Item Information

One of the main challenges for autonomous learning in virtual environments is finding the right material that fits students’ needs and supports their learning process. Personalized recommender systems partially solve this problem by suggesting online educational resources to students based on their preferences. However, in educational environments (which need a proper characterization of both users and educational resources), most existing recommendation algorithms either fail to include all the available information or use hybrid processes that do not exploit possible relationships between users and item features. This article presents a personalized recommender system for educational resources aimed at combining user and item information into a single mathematical model based on matrix factorization. As a result, estimated latent factors can provide insight into possible interactions between users and item features, improving the quality of the information retrieval process. We validated the proposed model on a real dataset that contains the ratings assigned by students from Universidad Nacional de Colombia and Universidade Feevale to educational resources in the Colombian Federation of Learning Object Repositories (FROAC in Spanish). User characterization included learning style and educational level, whereas item characterization (obtained from the objects’ metadata), included interactivity level, aggregation level and type, and resource format. These results, compared to those obtained when not all the available information is included, show that our method can improve the recommendation process.