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.

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 A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yibo Wang ◽  
Mingming Wang ◽  
Wei Xu
Keyword(s):  
Sentiment Analysis ◽  
Recommender System ◽  
Evaluation Criteria ◽  
Big Data Analytics ◽  
Mobile Environment ◽  
Movie Recommendation ◽  
Hybrid Recommender System ◽  
Preliminary Recommendation ◽  
Hybrid Recommender ◽  
Hybrid Recommendation

Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.

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

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.

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

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.

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