Recommendation systems have become more important since the widespread use of the Internet. The large amount of information means that it is difficult for users to discover what they really need. However, users’ preferences can change over time because of the age and the impact of social networks. Recommendation systems must capture users’ preferences and recommend suitable items, which is a difficult challenge. This study proposes a novel recommendation system that adapts to the changing preferences of users. By learning and adapting to users’ changing preferences better recommendations are provided. This study uses context factors as additional information to model users’ preferences more accurately. For the proposed recommendation system, the context is based on users’ most recent interaction items. This study proposes two novel attention mechanisms for the recommendation system. The contextual item attention module captures contextual information, changing pattern in users’ preference and the importance of items. The multi-head attention module extends the diversity of users’ preferences and adapts to changing preferences. The recommendation performance is improved using additional item's temporal information to model the contextual item's representation. Experiments compare the proposed algorithm with several state-of-the-art recommendation methods using three real-world datasets. The experimental results demonstrate that the proposed context-aware recommendation model outperforms traditional methods and demonstrate the effectiveness with which contextual information is captured by the attention mechanism.

Angle reciprocity is an important property adopted for massive MIMO channel estimation. For systems equipped with uniform rectangular antenna array, two-dimensional fast Fourier transform (2D-FFT) is often employed to transform the information in the spatial domain to the angular domain. To save the complexity, we propose hierarchical channel interpolation algorithm by exploiting the channel sparsity in the millimeter wave frequency band. Local interpolation with fine resolution is only applied to the selected regions determined from the coarse interpolation for obtaining required angular information so as to eliminate the unnecessary computations. Furthermore, the interference caused by the energy leakage from the adjacent paths is cancelled to acquire better estimation results of path gains. From the performance simulation results, the proposed algorithm can achieve better channel estimation performance than the zero-padded and phase-rotated 2D-FFT algorithms with reduced complexity for acquiring angular information.

mender system is studied widely and has been implemented successfully into businesses and daily lives. Its primary purpose is to analyze user behaviors to predict the next item of interest. To enrich user information, a session-based recommender system considers the user's historical records in the session. This information includes the user's general and current interests as additional information to improve the performance of the model such that the next item can be recommended easily. However, the recommender system not only considers the prediction performance but also regards the interpretability as a major target. For enhancing the interpretability and performance, we propose a novel mechanism, i.e., FuzzAttention, based on the attention mechanism that is applied widely to deep-learning models. In FuzzAttention, we utilize a fuzzy neural network to build a fuzzy inference system; therefore, we adopt joint learning to learn the parameters in the session-based recommendation and the fuzzy neural network jointly. In the experiments, we used two types of session-based recommender systems and conducted them on two datasets including the session-based information to compare the model performance based on the traditional attention mechanism and FuzzAttention. The results indicate that our proposed mechanism can improve the performance to predict the next item and the model's interpretability.

Fuzzy relationships exist between students’ learning performance with various abilities and a test item. However, the challenges in implementing adaptive assessment agents are obtaining sufficient items, efficient and accurate computerized estimation, and a substantial feedback agent. Additionally, the agent must immediately estimate students’ ability item by item, which places a considerable burden on the server, especially for a group test. Hence, the implementation of an adaptive assessment agent is more difficult in practice. This paper proposes an agent with particle swarm optimization (PSO) based on a fuzzy markup language (FML) for students’ learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory (IRT)-based three-parameter logistic model. First, we apply a Gauss–Seidel based parameter estimation mechanism to estimate the items’ parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PSO-based FML (PFML) learning mechanism for optimizing the parameters between items and students. Finally, we adopt aK-fold cross-validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important colearning mechanism for future human–machine educational applications.

Owing to advanced technical progress in information and communication technology, computerized adaptive assessment becomes more and more important for the personalized learning achievement. According to the response data from the conventional test and three-parameter logistic (3PL) model of the item response theory (IRT), this paper combines IRT with fuzzy markup language (FML) for an adaptive assessment application. The novel FML-based IRT estimation mechanism includes a Gauss-Seidel (GS) parameter estimation mechanism, a fuzzy knowledge base and a fuzzy rule base, to estimate the item parameters for each item. Meanwhile, it is able to infer the possibility of correct response to each item for each involved student. Additionally, this paper also proposes a static-IRT test assembly mechanism to assemble a form for the conventional test. After that, this paper chooses 5-fold cross validation to validate the research performance. From the experimental results, it shows that the proposed approach performs better than the traditional Bayesian estimation one.

Student's performance is classified into four levels, including below basic, basic, proficient, and advanced levels. The descriptions of the performance standard make students understand their learning achievement via percentile rank (PR), a norm-referenced score, and T score (I). This paper develops an adaptive student assessment system and invites elementary-school students to do a test in mathematics. Additionally, one adaptive item selection strategy mechanism is developed to choose next item that meets the student's current estimated ability. After that, the response data are collected to execute the type-2 fuzzy set (T2FS) construction mechanism to build a personalized T2FS for each student's performance and a T2FS for all students with an identical level. Finally, the student evaluation mechanism is executed to show students and teachers some useful information to assist in their future teaching and guidance. The simulation results show the proposed approach is feasible to adaptively select items from the item bank and construct T2FS for students' ability. In the future, we plan to use the technologies of optimization and computational intelligence to infer each student's ability in the test based on the constructed T2FSs.