ASSESSING RELATIONS AMONG LANDSCAPE PREFERENCE, INFORMATIONAL VARIABLES, AND VISUAL ATTRIBUTES

The theory of preference matrix proposes coherence and complexity as informational variables to explain landscape preferences. To understand the relationship between the perceived coherence/complexity and the visual attributes of landscape scenes, we constructed multivariate generalized linear models based on a questionnaire study. A total of 488 respondents’ ratings of the preference, the perceived coherence and complexity, and four visual attributes, namely, the openness of visual scale (openness), the richness of composing elements (richness), the orderliness of organization (orderliness), and the depth of view (depth), of a set of digitally manipulated landscape scenes were analyzed. The results showed that landscape preference needed to be explained with coherence and complexity together. Meanwhile, rather than showing the one-one connection with a single visual attribute, the degree of perceived coherence/complexity should be explained with multiple visual attributes. Ranked by explanatory power, the coherence was positively related to orderliness, negatively related to richness, and positively related to openness. The complexity was positively influenced by the level of richness, depth, and negatively influenced by orderliness and openness. Based on the results, feasible ways to build landscape environments with both preferable coherence and complexity were proposed.

Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

Comparative case study on system-integrated measurement technologies

The aim of the research is to make a comparison between system integrated measurement technologies in the field of engineering education in order to the students getting more detailed knowledge about the high level problem solving. A comparative case study was conducted with 3 different types of systems, as follows: Beckhoff, National Instruments, and HBM. The criteria of the systems are determined based on experience and the importance level of them was calculated by preference matrix. The ranks of the alternatives are calculated by Kesselring method, which provides the effectiveness value of the systems compared to the benchmark. The result of the paper shows a suitable method for selecting engineering systems.

AHP-Like Matrices and Structures—Absolute and Relative Preferences

Aggregation functions are extensively used in decision making processes to combine available information. Arithmetic mean and weighted mean are some of the most used ones. In order to use a weighted mean, we need to define its weights. The Analytical Hierarchy Process (AHP) is a well known technique used to obtain weights based on interviews with experts. From the interviews we define a matrix of pairwise comparisons of the importance of the weights. We call these AHP-like matrices absolute preferences of weights. We propose another type of matrix that we call a relative preference matrix. We define this matrix with the same goal—to find the weights for weighted aggregators. We discuss how it can be used for eliciting the weights for the weighted mean and define a similar approach for the Choquet integral.

A music recommendation algorithm based on clustering and latent factor model

The collaborative filtering recommendation algorithm is a technique for predicting items that a user may be interested in based on user history preferences. In the recommendation process of music data, it is often difficult to score music and the display score data for music is less, resulting in data sparseness. Meanwhile, implicit feedback data is more widely distributed than display score data, and relatively easy to collect, but implicit feedback data training efficiency is relatively low, usually lacking negative feedback. In order to effectively solve the above problems, we propose a music recommendation algorithm combining clustering and latent factor models. First, the user-music play record data is processed to generate a user-music matrix. The data is then analyzed using a latent factor probability model on the resulting matrix to obtain a user preference matrix U and a musical feature matrix V. On this basis, we use two K- means algorithms to perform user clustering and music clustering on two matrices. Finally, for the user preference matrix and the commodity feature matrix that complete the clustering, a user-based collaborative filtering algorithm is used for prediction. The experimental results show that the algorithm can reduce the running cost of large-scale data and improve the recommendation effect.

Formation of an effective management structure for enterprises in the energy sector of the economy

The aim of the study is to form an effective management structure for enterprises in the energy sector of the economy, operating in an unstable external environment. The result of the analysis of modern types of organizational structures is the proposed form of management structure, which takes into account the construction specifics. The developed set of guidelines for assessing the efficiency of the management structure can be applied not only in the construction industry but also in other industries, including in the energy sector. Special attention is paid to the analysis of external and internal factors affecting the activities of enterprises. Various structures of enterprise management are modeled using mathematical models and the preference matrix method when assessing the integral indicator of the management structure efficiency.