Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.

Interrelationship Mining from a Viewpoint of Rough Sets on Two Universes

We discuss connections between the interrelationship mining, proposed by the authors, and rough sets on two universes. The interrelationship mining enable us to extract characteristics based on comparison between values of different attributes. Rough sets on two universes is an theoretical extension of the original rough sets by considering connection between two universes. In this paper, we point out that interrelationship between different attributes in the interrelationship mining is representable by a variant of rough sets on two universes

Sand production simulation under true-triaxial stress conditions

Sand production in weakly consolidated sandstone reservoirs could result in damaging the production and surface facilities. Sanding includes two stages: the failure of sandstone around the borehole and sand grains being transported into the borehole. The first stage is related to stresses around the borehole whereas the second one is controlled by drawdown pressure. In order to avoid sanding, the stresses around the borehole and the drawdown pressure which initiate sanding are studied.This research simulated sand production through laboratory experiments and numerical simulations. The effect of three independent far-field stresses was investigated which is contrary to most of the current studies being performed under a uniaxial or triaxial stress state. Accordingly, a unique experimental setup and procedure was introduced to conduct sand production experiments under true-triaxial stress conditions. The effect of drawdown pressure and state of far-field stresses on the sanding mechanism and development of the failure zone around a borehole were investigated. The experiments were conducted on 100×100×100 mm3 cubic samples of synthetic sandstones. The samples were manufactured using an established procedure developed to produce samples with properties similar to weakly consolidated sandstone. The properties of the synthetic sandstone samples were determined by conducting a series of standard rock mechanics tests on cylindrical plugs. Using a true-triaxial stress cell (TTSC), cubic samples were subjected to three independent boundary stresses and uniform lateral fluid flow from the outer boundaries. The fluid flows through the sample radially and discharges from a hole drilled at the centre of the sample: this allows the study of sanding initiation by changing the state of stress, sample material and fluid properties.In this research, firstly, the concept of sand production from a geomechanics point of view and a summary of previous sanding experiments are explained. Thereafter, the procedure to prepare a sample suitable for sand production experiments is provided. Subsequently, the experimental equipment, setup and procedure are explained in detail. This is followed by presenting the results of two sets of experiments performed at different states of stress. The effect of changing the lateral stresses on the development of the failure zone around borehole was investigated in these experiments. During these experiments it was observed that a minimum drawdown pressure is needed to initiate sand production, regardless of the state of the boundary stresses. In addition, it was observed that the geometry (i.e. width and depth) of the failure zone developed around the borehole is a function of the state of stresses.The experiments were also simulated numerically using ABAQUS in order to gain a better understanding of sand production mechanisms. The numerical modelling procedure and results are presented in a separate section in this thesis. Good agreement was obtained between the results of both experimental and numerical methods which confirm the importance of the state of stresses on the evolution of sanding. Based on the experimental and numerical observations, it was shown that the effect of the magnitude of the maximum lateral stress on the depth of failure is more significant than the minimum lateral stress

Rough-Set-Based Interrelationship Mining for Incomplete Decision Tables

Rough-set-based interrelationship mining enables to extract characteristics by comparing the values of the same object between different attributes.To apply this interrelationship mining to incomplete decision tables with null values, in this study, we discuss the treatment of null values in interrelationships between attributes. We introduce three types of null values for interrelated condition attributes and formulate a similarity relation by such attributes with these null values

Extracting Business Intelligence from Online Product Reviews: An Experiment of Automatic Rule-Induction

Online product reviews are a major source of business intelligence (BI) that helps managers and market researchers make important decisions on product development and promotion. However, the large volume of online product review data creates significant information overload problems, making it difficult to analyze users’ concerns. In this paper, we employ a design science paradigm to develop a new framework for designing BI systems that correlate the textual content and the numerical ratings of online product reviews. Based on the framework, we developed a prototype for extracting the relationship between the user ratings and their textual comments posted on Amazon.com’s Web site. Two data mining algorithms were implemented to extract automatically decision rules that guide the understanding of the relationship. We report on experimental results of using the prototype to extract rules from online reviews of three products and discuss the managerial implications

Exploring the mutual influence among the social innovation factors amid the COVID-19 pandemic

From the triple bottom line, the social aspect has received relatively limited attention during the Corona Virus Disease (COVID-19) pandemic, particularly in the emerging economies. Social innovation factors help improve the sustainability performance of the companies. This study develops a social innovation decision framework and analyses the interrelationships among social innovation factors considering the COVID-19 situation. For this purpose, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) is extended by integrating the Z numbers and rough fuzzy set theory into its computational procedure. Z-numbers address the uncertainty of the decision and experts’ confidence in the evaluation and rough numbers are used for aggregating the experts’ opinions. On this basis, the mutual influence of social innovation factors and the influence weights of these factors are investigated. The results suggest that a quick response to market demand for sustainable products is the most influential factor in attaining social sustainability innovation during the pandemic. This article is concluded by providing insights for industrial experts and decision-makers to understand the underpinnings of social sustainability innovation during unforeseen situations

Geotechnical rock mass documentation in the Bátaapáti radioactive waste repository

The geologic, geotechnical and rock mechanical background of the area, which includes an underground cavern system, is very complex. Initially, to describe the rock mass properties, the RMR and Q systems were used, while the rock support system was based solely on the Q system. Later, during construction of the repository chambers, the GSI system was also introduced in addition to the above-mentioned rock classification methods. The correlation and usage of these three different rock mass classification systems were investigated.Special methods were used to obtain more accurate tunnel mapping and documentation, which is based on photogrammetry and photo analyzer software.According to the results, the correlation between the RMR and Q values is independent of the rock type; basically, the same equation can be used for all (differences between experimental error). The GSI intervals also closely follows the same trend as the results of the other two methods