scholarly journals Conversational Recommender System with Explanation Facility Using Semantic Reasoning

2016 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Zk Abdurahman Baizal ◽  
Nur Rahmawati
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
User Model ◽  
Functional Requirements ◽  
Inference Method ◽  
Semantic Reasoning ◽  
Filtering Method ◽  
Knowledge Based ◽  
The Relationship ◽  
Number Of Iterations

<p>Conversational recommender system is system that provides dialogue as user guide to obtain information from the user, in order to obtain preference for products needed. This research implements conversational recommender system with knowledge-based in the smartphone domain with an explanation facility. The recommended products are obtained based on the functional requirements of the user. Therefore, this study use ontology model as a knowledge to be more flexible in finding products that is suitable with the functional requirements of the user that is by tracing a series of semantic based on relationships in order to obtain the user model. By exploiting the relationship between instances of user models, the explanation facility generated can be more natural. Our filtering method uses semantic reasoning with inference method to avoid overspecialization. The evaluation show that the performance of our recommender system with explanation facilities is more efficient than the recommendation system without explanation facility, that can be seen from the number of iterations. We also notice that our system has accuracy of 84%.</p>

A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

2018 ◽  
Vol 9 (4) ◽  
pp. 38-51
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
User Model ◽  
Context Aware ◽  
Additional Information ◽  
Context Element ◽  
Cold Start Problem

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

Challenges and Applications of Recommender Systems in E-Commerce

2021 ◽  
pp. 175-188
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Md Imran Hussain
Keyword(s):  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Recommendation System ◽  
Main Idea ◽  
Important Task ◽  
Significant Challenge ◽  
Online Business ◽  
The One ◽  
The Relationship

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.

scholarly journals Community-Driven Collaborative Recommendation System

2019 ◽  
Vol 8 (4) ◽  
pp. 3722-3726
Keyword(s):  
Recommendation System ◽  
Singular Value ◽  
Daily Lives ◽  
Filtering Method ◽  
Singular Value Decomposition Method ◽  
Collaborative Recommendation ◽  
Value Decomposition ◽  
Movie Ratings ◽  
The Comparative Study ◽  
The Relationship

Recommendation systems (RSs) are an application of community detection, becoming more significant in our daily lives. They play a significant role in suggesting information to users such as products, services, friends and so on. A novel community driven collaborative recommendation system (CDCRS) has been proposed by the authors, in this particular paper. Furthermore, K means approach has been utilized to detect communities and extract the relationship among the users. The singular value decomposition method (SVD) is also applied. Issues of sparsity and scalability of the collaborative method are considered. Experiments were conducted on MovieLens datasets. Movie ratings were predicted and top-k recommendations for the user produced. The comparative study that was performed between the proposed as well as the collaborative filtering method dependent on SVD (CFSVD) as well as the results of experiments shows that CFSVD is outperformed by the proposed CDCRS method.

scholarly journals The Design of Web Based Car Recommendation System using Hybrid Recommender Algorithm

2018 ◽  
Vol 7 (3.4) ◽  
pp. 192
Author(s):  
Leyo Babu Thomas ◽  
V Vaidhehi
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Web Sites ◽  
Recommendation System ◽  
User Model ◽  
Web Based ◽  
Filtering Method ◽  
Recommender Algorithm ◽  
Hybrid Recommender ◽  
Demographic Features

Web based recommendations for any item is mandatory in E-commerce based web sites. This paper is about the design of web based car recommendation system using the hybrid recommender algorithm. The proposed hybrid recommender algorithm is the combination of user-to-user and item-to-item collaborative filtering method to generate the car recommendations. The user model is designed using demographic features, click data and browsing history. Item profile is built using the various attributes of car, 40 brands of car including 224 car types are used in this work. The synthetic dataset of 300 users with 10000 sessions is used to build user model. The proposed algorithm is evaluated with 100 real time users and shows the 83% accuracy in generating recommendations.  

Development of a dress design knowledge base (DDKB) based on sensory evaluation and fuzzy logic

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue Ling ◽  
Yan Hong ◽  
Zhijuan Pan
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
Sensory Evaluation ◽  
Recommendation System ◽  
Evaluation Procedure ◽  
Design Knowledge ◽  
Content Type ◽  
Knowledge Based ◽  
Element Matrix ◽  
The Relationship

PurposeThe purpose of this paper is to develop a dress design knowledge base (DDKB), which is expected to be further applied to a personalized dress recommendation system.Design/methodology/approachDress design knowledge can be expressed as the relationship between designer's fashion perceptions of different dress elements. In order to extract dress design knowledge, a dress shape ontology (DSO) is firstly developed, which can be further used to form a dress element matrix (DEM). A perceptual descriptive space of the dress (DPDS) is developed for the description of the designer's fashion perception of dress. Through a standard sensory evaluation procedure performed by experienced experts (designers), the expected relationship can be obtained. This relationship is then mathematically simulated by fuzzy logic tools for the expected DDKB.FindingsIn this paper, a DDKB has been developed. The established knowledge base has been validated, and it can be further applied to dress recommendation system for a specific consumer.Originality/valueThis study introduces the concept of knowledge base to the area of dress individualized design. The knowledge-based design process based on sensory evaluation and fuzzy logic can efficiently solve the individualization of dress design in traditional design processes, which can provide a novel way to dress design individualization.

scholarly journals Towards a Knowledge-Based Recommendation System for Genomics Visualization

2020 ◽  
Author(s):  
Aditeya Pandey ◽  
Sehi L’Yi ◽  
Nils Gehlenborg
Keyword(s):  
Data Visualization ◽  
Biomedical Research ◽  
Recommender System ◽  
Recommendation System ◽  
Formal Training ◽  
Preliminary Design ◽  
Knowledge Based ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Selection Of

Analysis and interpretation of genomics data are the backbones of breakthroughs and discoveries in biomedical research. Visualization tools and techniques play a significant role in the workflow of genomics researchers, and they are regularly employed in the interpretation of genomics data. However, the vast majority of genomics researchers have little or no formal training in data visualization design. Therefore, they require guidance on designing effective visualizations for a given set of data and analysis tasks. In this poster, we present the methodology behind a recommender system for genomics data and our preliminary design of a knowledge-based recommendation system. The system allows genomics researchers to navigate through a selection of visualization options and identify the techniques that meet their preferences and requirements.

scholarly journals ALGORITHMIC SUPPORT OF THE WEB SERVICE RECOMMENDATION SYSTEM FOR LEARNING FOREIGN LANGUAGES

2021 ◽  
pp. 100-106
Author(s):  
Liliia Bodnar ◽  
Kateryna Shulakova ◽  
Liudmyla Gryzun
Keyword(s):  
Foreign Language ◽  
Web Service ◽  
Recommender System ◽  
Recommendation System ◽  
Foreign Languages ◽  
Multimedia Content ◽  
Functional Requirements ◽  
Algorithmic Support ◽  
User Ratings ◽  
Selection Of

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.

An Integrated Approach for a Knowledge-based Clinical Workstation: Architecture and Experience

1998 ◽  
Vol 37 (01) ◽  
pp. 16-25 ◽  
Author(s):  
P. Ringleb ◽  
T. Steiner ◽  
P. Knaup ◽  
W. Hacke ◽  
R. Haux ◽  
...  
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Patient Care ◽  
Hospital Information Systems ◽  
Medical Decision ◽  
Daily Routine ◽  
Functional Requirements ◽  
Support Functions ◽  
Knowledge Based ◽  
Clinical Workstation

Abstract:Today, the demand for medical decision support to improve the quality of patient care and to reduce costs in health services is generally recognized. Nevertheless, decision support is not yet established in daily routine within hospital information systems which often show a heterogeneous architecture but offer possibilities of interoperability. Currently, the integration of decision support functions into clinical workstations is the most promising way. Therefore, we first discuss aspects of integrating decision support into clinical workstations including clinical needs, integration of database and knowledge base, knowledge sharing and reuse and the role of standardized terminology. In addition, we draw up functional requirements to support the physician dealing with patient care, medical research and administrative tasks. As a consequence, we propose a general architecture of an integrated knowledge-based clinical workstation. Based on an example application we discuss our experiences concerning clinical applicability and relevance. We show that, although our approach promotes the integration of decision support into hospital information systems, the success of decision support depends above all on an adequate transformation of clinical needs.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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