A Fuzzy-Based Recommender System for Electronic Products Selection using Users' Requirements and Other Users' Opinion

2015 ◽  
Vol 4 (1) ◽  
pp. 76-87 ◽  
Author(s):  
Bolanle Adefowoke Ojokoh ◽  
Olatunji Mumini Omisore ◽  
Oluwarotimi Williams Samuel ◽  
Temidayo Otunniyi
Keyword(s):  
Fuzzy Logic ◽  
Recommender System ◽  
Experimental Results ◽  
Electronic Products ◽  
Hybrid Recommender System ◽  
The Rich ◽  
Rich Data ◽  
Hybrid Recommender ◽  
The Web ◽  
Experience Difficulty

E-Commerce has become very popular these days because it is convenient, reliable, and fast to use. In spite of these advantages, online buyers often experience difficulty in searching for products on the web, while online businesses are often overwhelmed by the rich data they have collected and find it difficult to promote products appropriate to specific customers. This paper proposes a hybrid recommender system that uses fuzzy logic to intelligently mine the requirements of each specific customer, together with some previous users' opinions about the product, to recommend a list of optimal products to meet users' needs. Experimental results of the proposed system with different brands of laptops prove its effectiveness.

Intelligent Techniques in Recommender Systems and Contextual Advertising

2013 ◽  
pp. 105-128 ◽  
Author(s):  
Giuliano Armano ◽  
Alessandro Giuliani ◽  
Eloisa Vargiu
Keyword(s):  
Recommender System ◽  
Information Filtering ◽  
Relevant Information ◽  
Web Page ◽  
Contextual Advertising ◽  
Generic Hybrid ◽  
Unified View ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
The Web

Information Filtering deals with the problem of selecting relevant information for a given user, according to her/his preferences and interests. In this chapter, the authors consider two ways of performing information filtering: recommendation and contextual advertising. In particular, they study and analyze them according to a unified view. In fact, the task of suggesting an advertisement to a Web page can be viewed as the task of recommending an item (the advertisement) to a user (the Web page), and vice versa. Starting from this insight, the authors propose a content-based recommender system based on a generic solution for contextual advertising and a hybrid contextual advertising system based on a generic hybrid recommender system. Relevant case studies have been considered (i.e., a photo recommender and a Web advertiser) with the goal of highlighting how the proposed approach works in practice. In both cases, results confirm the effectiveness of the proposed solutions.

The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF

2021 ◽  
Vol 5 (5) ◽  
pp. 977-983
Author(s):  
Muhammad Johari ◽  
Arif Laksito
Keyword(s):  
Recommender System ◽  
Web Application ◽  
Information Filtering ◽  
Modern Society ◽  
The Internet ◽  
Hybrid Filtering ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Content Based Filtering ◽  
The Web

Today, consumers are faced with an abundance of information on the internet; accordingly, it is hard for them to reach the vital information they need. One of the reasonable solutions in modern society is implementing information filtering. Some researchers implemented a recommender system as filtering to increase customers’ experience in social media and e-commerce. This research focuses on the combination of two methods in the recommender system, that is, demographic and content-based filtering, commonly it is called hybrid filtering. In this research, item products are collected using the data crawling method from the big three e-commerce in Indonesia (Shopee, Tokopedia, and Bukalapak). This experiment has been implemented in the web application using the Flask framework to generate products’ recommended items. This research employs the IMDb weight rating formula to get the best score lists and TF-IDF with Cosine similarity to create the similarity between products to produce related items.  

scholarly journals Improving Children’s Experience on a Mobile EdTech Platform through a Recommender System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Almudena Ruiz-Iniesta ◽  
Luis Melgar ◽  
Alejandro Baldominos ◽  
David Quintana
Keyword(s):  
Collaborative Filtering ◽  
Mobile Devices ◽  
Recommender System ◽  
Experimental Results ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Smile and Learn is an EdTech digital publisher that offers a smart library of close to 100 educational stories and gaming apps for mobile devices aimed at children aged 2 to 10 and their families. Given the complexity of navigating the content, a recommender system was developed. The system consists of two major components: one that generates content recommendations and another that provides explanations and recommendations relevant to parents and educators. The former was implemented as a hybrid recommender system that combines three kinds of recommendations. Among these, we introduce a collaborative filtering adapted to overcome specific limitations associated with younger users. The approach described in this work was tested on real users of the platform. The experimental results suggest that this recommendation model is suitable to suggest apps to children and increase their engagement in terms of usage time and number of games played.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

scholarly journals ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets

2012 ◽  
Vol 22 (4) ◽  
pp. 801-816 ◽  
Author(s):  
Ricardo Colomo-Palacios ◽  
Israel González-Carrasco ◽  
José Luis López-Cuadrado ◽  
Ángel García-Crespo
Keyword(s):  
Software Development ◽  
Recommender System ◽  
Rough Set Theory ◽  
Project Managers ◽  
Agile Development ◽  
Project Leader ◽  
Development Processes ◽  
Hybrid Recommender System ◽  
Real Scenario ◽  
Hybrid Recommender

Agile development is a crucial issue within software engineering because one of the goals of any project leader is to increase the speed and flexibility in the development of new commercial products. In this sense, project managers must find the best resource configuration for each of the work packages necessary for the management of software development processes in order to keep the team motivated and committed to the project and to improve productivity and quality. This paper presents ReSySTER, a hybrid recommender system based on fuzzy logic, rough set theory and semantic technologies, aimed at helping project leaders to manage software development projects. The proposed system provides a powerful tool for project managers supporting the development process in Scrum environments and helping to form the most suitable team for different work packages. The system has been evaluated in a real scenario of development with the Scrum framework obtaining promising results.

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