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.  

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.

scholarly journals Personalized Recommender System for Calculus using Content-Based Filtering Approach

2018 ◽  
Vol 7 (3.15) ◽  
pp. 110 ◽  
Author(s):  
Noor Latiffah Adam ◽  
Muhammad Alif Zulkafli ◽  
Shaharuddin Cik Soh ◽  
Nor Ashikin Mohamad Kamal ◽  
Nordin Abu Bakar
Keyword(s):  
Recommender System ◽  
The Internet ◽  
Internet Users ◽  
Basic Calculus ◽  
Personalized Recommender System ◽  
Many Sources ◽  
Content Based Filtering ◽  
Human Kind ◽  
The Web ◽  
Filtering Approach

In this millennial age, Internet is becoming essential to human kind. Along with the growth of Internet users, information is also becoming huge and starting to cause difficulties to find the relevant contents. Thus, the recommender system was introduced. It helps the user to suggest the items based on the user’s preferences. This system could help the students as Calculus is one of the tough subjects feared by most students. Credits given to the technology as many sources on the web can provide tutorials, working examples and solutions on the subjects. However, there are too many of them. Students had to make a few selections, which one can fulfil their needs of specific calculus topics. The personalized recommender system developed was a content-based filtering recommender system with its own scraping engine to collect the sources from the Internet which focuses on the basic Calculus topics. The system and engine were constructed by using Flask framework together with its relevant libraries. 

scholarly journals A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce

2021 ◽  
Vol 9 (11) ◽  
pp. 635-638
Author(s):  
Dr. C. K. Gomathy
Keyword(s):  
Collaborative Filtering ◽  
Language Processing ◽  
Recommender System ◽  
Matrix Factorization ◽  
Mean Square ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Traditional Approaches ◽  
Content Based Filtering ◽  
Previous User

Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis

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.

scholarly journals Developing a Contextually Personalized Hybrid Recommender System

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Aysun Bozanta ◽  
Birgul Kutlu
Keyword(s):  
Recommender System ◽  
Real World ◽  
Experimental Evaluation ◽  
User Study ◽  
Contextual Information ◽  
Threshold Values ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Hybrid Recommendation ◽  
Content Based Filtering

It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

scholarly journals A Novel Model for Explainable Hostel Recommender System Using Hybrid Filtering

2021 ◽  
Vol 5 (2) ◽  
pp. 1-12
Author(s):  
Shahzad Ahmed Khan
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Contextual Information ◽  
Hybrid Filtering ◽  
Enormous Amount ◽  
Hybrid Recommender System ◽  
The Right ◽  
Hybrid Recommender ◽  
Novel Model

Recommender systems help humans in filtering and finding the right information from the enormous amount of data. Hostels are more famous than hotels for solo travelers, but no prior research related to recommender systems has been conducted in this domain. Hostels allow users to provide multi-criteria ratings and traditional recommender systems are not able to provide effective recommendations in case of multi-dimensionality i.e. contextual information and multi-criteriaratings. So, we have proposed a novel hybrid recommender system (SAFCHERS) that chooses the hostel's features for computation dynamically and provides explainable and better recommendations than the traditional recommender systems.

On the integration of Machine Learning algorithms and operations research techniques in the development of a hybrid Recommender System

2021 ◽  
pp. 1-14
Author(s):  
Panagiotis Giannopoulos ◽  
Georgios Kournetas ◽  
Nikos Karacapilidis
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Learning Algorithms ◽  
Information Filtering ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

scholarly journals Applying Neural Network Model to Hybrid Tourist Attraction Recommendations

2014 ◽  
Vol 6 (2) ◽  
pp. 63-69
Author(s):  
Marcelli Indriana ◽  
Chein-Shung Hwang
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Recommender System ◽  
Training Dataset ◽  
Target Value ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Content Based Filtering

Recently, recommender systems have been developed for a variety of domains. Recommender systems also can be applied in tourism industry to help tourists organizing their travel plans. Recommender systems can be developed by a variety of different techniques such as Content-Based filtering (CB), Collaborative filtering (CF), and Demographic filtering (DF). However, the uses of these techniques individually will have some disadvantages. In this research, we propose a hybrid recommender system to combine the predictions from CB, CF and DF approaches using neural network model. Neural network model will learn by processing a training dataset, comparing the network’s prediction for each dataset with the actual known target value. For each training dataset, the weights are modified to minimize the mean-squared error between the network’s prediction and the actual target value. The experimental results showed that the neural network model outperforms each individual recommendation techniques. Index Terms - Colaborative Filtering, Content-based filtering, Data Mining, Demographic Filtering, Hybrid Recommender System, Neural Network

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