The Development of Building Materials Recommendation System Based on Collaborative Filtering

2013 ◽  
Vol 281 ◽  
pp. 597-602 ◽  
Author(s):  
Guo Fang Kuang ◽  
Chun Lin Kuang
Keyword(s):  
Collaborative Filtering ◽  
Building Materials ◽  
Recommendation System ◽  
High Strength ◽  
Data Sets ◽  
User Interest ◽  
Insulation Materials ◽  
Target User ◽  
Wide Range ◽  
Materials Used

The building materials used in building materials collectively referred to as building materials. New building materials, including a wide range of insulation materials, insulation materials, high strength materials, breathing material belong to the new material. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of building materials recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals The Development of E-Commerce Recommendation System Based on Collaborative Filtering

2012 ◽  
Vol 6-7 ◽  
pp. 636-640 ◽  
Author(s):  
Guo Fang Kuang
Keyword(s):  
Experimental Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Product Information ◽  
Data Sets ◽  
User Evaluation ◽  
User Interest ◽  
Target User ◽  
Purchase Process ◽  
Sales Staff

The recommendation system in the e-commerce is to provide customers with product information and recommendations to help customers decide what to buy goods and analog sales staff to recommend merchandise to complete the purchase process. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of E-commerce recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items

2020 ◽  
Vol 8 (5) ◽  
pp. 01-15
Author(s):  
Dasong Sun ◽  
Shuqing Li ◽  
Wenjing Yan ◽  
Fusen Jiao ◽  
Junpeng Chen
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Memory Function ◽  
Three Dimensional ◽  
Matrix Decomposition ◽  
Data Sets ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Trust Network ◽  
Experimental Conditions

The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions.

scholarly journals COUNTRY HOUSE IN STYLE GLASS FACHWERK

2020 ◽  
Vol 11 (1) ◽  
pp. 115-124
Author(s):  
G. I Zubareva
Keyword(s):  
Building Materials ◽  
High Strength ◽  
Modern Technology ◽  
Solar Ultraviolet Radiation ◽  
Country House ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Country Houses ◽  
Materials Used ◽  
Definition Of

The urgency of the design and construction of country houses in the style of fachwerk is noted. The definition of fachwerk is given. The system of criteria characterizing the fachwerk is listed: the presence of a frame with braces and enclosing filling with various materials: clay, ceramic brick, natural stone. It is noted that glass is one of the most popular materials used in construction, including individual houses. The concept of glass fachwerk as a frame with glazing elements is defined. It is noted that with the advent of new building materials and tools, the construction of country houses using the glass fachwerk technology underwent many changes that affected virtually all the elements of the house's construction: foundation, frame, joint system, roof, roof and glass. The modern technology for the construction of glass fachwerk is described. The requirements for double-glazed windows fachwerk are discussed: high strength, increased sound and heat insulation, protection from solar ultraviolet radiation. A wide range of double-glazed windows satisfying these requirements is given: sun-reflecting, energy-saving, multifunctional and safe (triplex) glasses. The advantages and disadvantages of suburban half-timbered glass houses are discussed. It is shown that the individuality of a glass-fachwerk country house is achieved by the variability of its glazing: frame, frameless, and also depending on the percentage of glazing at home. The conclusion is made about the prospects of country houses in the style of glass half-timbered for the regions of Russia, taking into account the use of new building materials.

scholarly journals Protection as a Mean to Increase the Sustainability of Wood Structures

2012 ◽  
Vol 69 (2) ◽  
Author(s):  
Calin CORDUBAN ◽  
Giovanna BOCHICCHIO ◽  
Andrea POLASTRI ◽  
Ario CECCOTTI
Keyword(s):  
Fire Protection ◽  
Building Material ◽  
Building Materials ◽  
Sustainability Assessment ◽  
Financial Loss ◽  
Wood Structures ◽  
Fire Propagation ◽  
Wide Range ◽  
Timber Constructions ◽  
Materials Used

Timber has been rediscovered as the building material of choice in recent years, especially in industrialised countries, with the shift of focus on attitudes towards sustainability that include use of natural resources and reduction of CO 2 emissions in manufacturing building materials. The environmental qualities of wood (energy-efficiency, healphy building material, ability to be recycled) are matched by few materials used in constructions nowadays, makeing it suitable for a wide range of applications. The combustibility of wood is limiting its use in construction, an important weakness in terms of sustainability, as health and cost issues constitute essential conditions in sustainability assessment methods. Arguably, fire safety constitutes the foremost precondition in choosing wood as the building material. In the case of fire, wood burns on the surface, releases energy and contributes to the fire propagation and spread of smoke. In order to insure greater safety for timber constructions, both passive and active measures of fire protection can be implemented, with the main objectives of improving the security of occupants, limitations of financial loss, protection of the environment in the case of fire. Despite the fear of using wood, the material has a better behavior in terms of fire than assumed, and even with structures more susceptible at fire risks, such as platform framing, measures can be taken in order to improve safety, as further explained in the article. The article analyses the concept of sustainability and the extent to which timber constructions observe these criteria, focusing on the means of increasing safety by fire protection methods with respect to the environment.

scholarly journals MINING TRUST VALUES FROM RECOMMENDATION ERRORS

2006 ◽  
Vol 15 (06) ◽  
pp. 945-962 ◽  
Author(s):  
JOHN O'DONOVAN ◽  
BARRY SMYTH
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction ◽  
Information Needs ◽  
Recommendation System ◽  
Predictive Accuracy ◽  
Target User ◽  
Fair Comparison ◽  
Collaborative Filtering Algorithm ◽  
History Of ◽  
User Trust

Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user's history of contributions to recommendations. We compute a recommendation range to present to a target user. This is done by leveraging under/overestimate errors in users' past contributions in the recommendation process. We present three different models to compute this range. Our evaluation shows how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and we define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy. We aim to show that the presentation of absolute rating predictions to users is more likely to reduce user trust in the recommendation system than presentation of a range of rating predictions. To evaluate the trust benefits resulting from the transparency of our recommendation range techniques, we carry out user-satisfaction trials on BoozerChoozer, a pub recommendation system. Our user-satisfaction results show that the recommendation range techniques perform up to twice as well as the benchmark.

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

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenning Yuan ◽  
Jong Han Lee ◽  
Sai Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Preferences ◽  
Classification Model ◽  
Filtering Algorithm ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Low Dimensional ◽  
Preference Matrix

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.

scholarly journals Hydrothermally treated cement-based building materials. Past, present, and future

2002 ◽  
Vol 74 (11) ◽  
pp. 2131-2135 ◽  
Author(s):  
A. Ray
Keyword(s):  
Building Materials ◽  
Analytical Techniques ◽  
Drying Shrinkage ◽  
High Strength ◽  
Thermal Methods ◽  
Building Products ◽  
Blended Cements ◽  
Phase Development ◽  
Wide Range

Hydrothermally cured or autoclaved cement-based building products have provided many challenges to researchers, manufacturers, and users since their inception nearly 100 years ago. The advantages, including the development of high strength within a few hours and a reduction of drying shrinkage, of the hydrothermal curing process have resulted in a variety of building products; inevitably, the technology of their production has undergone many stages of refinement. With the advent of nonconventional starting materials for the production of modern cements, and the push to utilize renewable resources to form blended cements, the chemical and physical make-up of hydrothermally cured building materials have changed considerably in recent years and will continue to change. It is, therefore, important to understand the chemical reactions taking place in an autoclave, and the consequent phase developments, if building materials produced by this process continue to be successful in the long term. A wide range of analytical techniques exists for characterizing the phase development in cement-based materials. The purpose of this paper is to illustrate the strength of thermal methods, especially when used in combination with other analytical techniques, in the understanding of hydrothermal reactions.

Experimental Study on the Thermal Conductivity of Thermal Insulation Materials Used in Residential Buildings

2014 ◽  
Vol 1025-1026 ◽  
pp. 535-538
Author(s):  
Young Sun Jeong
Keyword(s):  
Thermal Conductivity ◽  
Thermal Insulation ◽  
Building Materials ◽  
Residential Buildings ◽  
Building Envelope ◽  
Expanded Polystyrene ◽  
Exterior Wall ◽  
Insulation Materials ◽  
Design Documents ◽  
Materials Used

The most basic way to keep comfortable indoor environments for a building’s occupants and save energy for space heating and cooling in residential buildings is to insulate the building envelope. Among the building materials to be used, thermal insulation materials primarily influence thermal performance. In particular, the type, thermal conductivity, density, and thickness of heat insulator, are important factors influencing thermal insulation performance. We investigate the design status of residential buildings which were designed in accordance with the building code of Korea and selected the type of thermal insulation materials applied to the walls of buildings. The present study aims at measuring the thermal conductivity of thermal insulation materials used for building walls of residential buildings. In this study, after collecting the design documents of 129 residential buildings, we investigated the type and thickness of insulation materials on the exterior wall specified in the design documents. As the thermal insulation materials, extruded polystyrene (XPS) board and expanded polystyrene(EPS) board are used the most widely in Korea when designing residential buildings. The thickness of thermal insulation materials applied to the exterior wall was 70mm, most frequently applied to the design. We measured the thermal conductivity and the density of XPS board and EPS board. When the density of XPS and EPS was 30~35 kg/㎥, the thermal conductivity of XPS was 0.0292 W/mK and it of EPS was 0.0316 W/mK.

scholarly journals Hybrid Measuring the Similarity Value Based on Genetic Algorithm for Improving Prediction in A Collaborative Filtering Recommendation System.

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Muaadh Abdo Mohammed Ahmed AL sabri
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Forecast Accuracy ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Personalized Recommendation ◽  
Education Economics ◽  
Standard Methods ◽  
Wide Range ◽  
Suitable Product

In recent years, the Recommendation System (RS) has a wide range of applications in several fields, like Education, Economics, Scientific Researches and other related fields. The Personalized Recommendation is effective in increasing RS's accuracy, based on the user interface, preferences and constraints seek to predict the most suitable product or services. Collaborative Filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. Various experts in the field are using methods like Nearest Neighbors (NN) based on the measures of similarity.  However, similarity measures use only the co-rated item ratings while calculating the similarity between a pair of users or items. The two standard methods used to measure similarities are Cosine Similarity (CS) and Person Correlation Similarity (PCS). However, both are having drawbacks, and the present piece of the investigation will approach through the optimized Genetic Algorithms (GA) to improve the forecast accuracy of RS using the merge output of CS with PCS based on GA methods. The results show GA's superiority and its ability to achieve more correct predictions than CS and PCS.

scholarly journals Event driven Recommendation System for E-commerce using Knowledge based Collaborative Filtering Technique

2020 ◽  
Vol 21 (3) ◽  
pp. 369-378
Author(s):  
Mahesh Kumar Singh ◽  
Om Prakash Rishi
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Recommendation System ◽  
Evaluation Studies ◽  
Knowledge Graph ◽  
Knowledge Based ◽  
Target User ◽  
Online Trading ◽  
User Data ◽  
Two Phases

The Internet is changing the method of selling and purchasing items. Nowadays online trading replaces offline trading. The items offered by the online system can influence the nature of buying customers. The recommendation system is one of the basic tools to provide such an environment. Several techniques are used to design and implement the recommendation system. Every recommendation system passes from two phases similarity computation among the users or items and correlation between target user and items. Collaborative filtering is a common technique used for designing such a system. The proposed system uses a knowledge base generated from knowledge graph to identify the domain knowledge of users, items, and relationships among these, knowledge graph is a labelled multidimensional directed graph that represents the relationship  among the users and the items. Almost every existing recommendation system is based on one of feature, review, rating, and popularity of the items in which users’ involvement is very less or none. The proposed approach uses about 100 percent of users’ participation in the form of activities during navigation of the web site. Thus, the system expects under the users’ interest that is beneficial for both seller and buyer. The proposed system relates the category of items, not just specific items that may be interested in the users. We see the effectiveness of this approach in comparison with baseline methods in the area of recommendation system using three parameters precision, recall, and NDCG through online and offline evaluation studies with user data, and its performance is better than all other baseline systems in all aspects.

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