Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network

2019 ◽  
pp. 121-143 ◽  
Author(s):  
Arisara Pornwattanavichai ◽  
Prawpan Brahmasakha na sakolnagara ◽  
Pongsakorn Jirachanchaisiri ◽  
Janekhwan Kitsupapaisan ◽  
Saranya Maneeroj
Keyword(s):  
Neural Network ◽  
Recommender System ◽  
Matrix Factorization ◽  
Topic Modeling ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

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 Hybrid Movie Recommender System and Rating Prediction Model

2021 ◽  
Vol 3 (3) ◽  
pp. 161-168
Author(s):  
Muhammad Sanwal ◽  
Cafer ÇALIŞKAN
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Matrix Factorization ◽  
Support Vector ◽  
Learning Methods ◽  
Long Time ◽  
Data Volume ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

In the current era, a rapid increase in data volume produces redundant information on the internet. This predicts the appropriate items for users a great challenge in information systems. As a result, recommender systems have emerged in this decade to resolve such problems. Various e-commerce platforms such as Amazon and Netflix prefer using some decent systems to recommend their items to users. In literature, multiple methods such as matrix factorization and collaborative filtering exist and have been implemented for a long time, however recent studies show that some other approaches, especially using artificial neural networks, have promising improvements in this area of research. In this research, we propose a new hybrid recommender system that results in better performance. In the proposed system, the users are divided into two main categories, namely average users, and non-average users. Then, various machine learning and deep learning methods are applied within these categories to achieve better results. Some methods such as decision trees, support vector regression, and random forest are applied to the average users. On the other side, matrix factorization, collaborative filtering, and some deep learning methods are implemented for non-average users. This approach achieves better compared to the traditional methods.

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.

Sign in / Sign up

Export Citation Format

Share Document