Recommender Systems Using Collaborative Tagging

2020 ◽  
Vol 16 (3) ◽  
pp. 183-200
Author(s):  
Latha Banda ◽  
Karan Singh ◽  
Le Hoang Son ◽  
Mohamed Abdel-Basset ◽  
Pham Huy Thong ◽  
...  
Keyword(s):  
Social Networking ◽  
Recommender Systems ◽  
Recommender System ◽  
Prediction Accuracy ◽  
Clustering Algorithms ◽  
Incremental Clustering ◽  
Collaborative Tagging ◽  
Accuracy Ratio ◽  
Hybrid Recommender ◽  
Gradual Decay

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

scholarly journals A Hybrid Recommender System Based on User-Recommender Interaction

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Heng-Ru Zhang ◽  
Fan Min ◽  
Xu He ◽  
Yuan-Yuan Xu
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Hybrid Algorithm ◽  
Nearest Neighbor ◽  
User Behavior ◽  
User Choice ◽  
User Request ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Diversity Metrics

Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction. The recommender system accepts user request, recommendsNitems to the user, and records user choice. If some of these items favor the user, she will select one to browse and continue to use recommender system, until none of the recommended items favors her. Second, we propose a hybrid recommender system combining random andk-nearest neighbor algorithms. Third, we redefine the recall and diversity metrics based on the new scenario to evaluate the recommender system. Experiments results on the well-known MovieLens dataset show that the hybrid algorithm is more effective than nonhybrid ones.

A Hybrid Recommender System for Rating Prediction of Arabic Reviews

2020 ◽  
pp. 2050010
Author(s):  
Fouzi Harrag ◽  
Abdulmalik Salman Al-Salman ◽  
Alaa Alquahtani
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Opinion Mining ◽  
Good Accuracy ◽  
Hybrid Approach ◽  
Data Sparsity ◽  
Hybrid Recommender System ◽  
Rating Prediction ◽  
Hybrid Recommender

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users’ reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85% in predicting the rating from reviews.

Evaluating Prediction Accuracy, Developmental Challenges, and Issues of Recommender Systems

2018 ◽  
Vol 10 (2) ◽  
pp. 61-79 ◽  
Author(s):  
J. Sharon Moses ◽  
L.D. Dhinesh Babu
Keyword(s):  
Social Media ◽  
Recommender Systems ◽  
Recommender System ◽  
Prediction Accuracy ◽  
Recommendation Systems ◽  
Geographical Location ◽  
The Internet ◽  
The People ◽  
Implicit And Explicit ◽  
The Internet Of Things

Modern ways of living have made the people to depend on internet services for everything. The mounting information from various sources like social media, implicit and explicit information, user's geographical location, and the internet of things had increased the need of a recommender system. From e-governance to e-shopping, a recommender system helps people in finding the needed item or information and also boosts sales in the market of those items. Though many studies elaborate about recommendation systems, challenges in developing the recommendation systems, prevailing issues of recommendation systems and discussions on prediction accuracy are not detailed in any of the earlier works. Therefore, in this article, in order to increase the accuracy of the recommender system, the developmental challenges and issues in constructing recommender systems and for evaluation metrics in prediction accuracy are identified and detailed.

Hybrid Recommender System Using Emotional Fingerprints Model

2019 ◽  
Vol 9 (3) ◽  
pp. 48-70
Author(s):  
Anthony Nosshi ◽  
Aziza Saad Asem ◽  
Mohammed Badr Senousy
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Experimental Simulation ◽  
Key Factors ◽  
Easy Task ◽  
Needed Information ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.

scholarly journals Exploring the effects of Clustering Algorithms on Free Text Recommendation

2018 ◽  
Author(s):  
Israel Mendonca dos Santos ◽  
Antoine Trouve ◽  
Akira Fukuda ◽  
Kazuaki Murakami
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Negative Impact ◽  
Clustering Algorithms ◽  
Search Space ◽  
Free Text ◽  
Word Embeddings ◽  
Performance Measurements ◽  
Number Of Clusters

In this paper, we provide a study on the effects of applying classical clustering algorithms, such as k-Means to free text recommender systems. A typical recommender system may face problems when the number of items from a database goes from a few items to hundreds of items. Currently, one of the most prominent techniques to scale the database is applying clustering, however clustering may have a negative impact on the accuracy of the system when applied without taking into consideration the underlying items. In this work, we build a conceptual text recommender system and use k-Means to partition its search space into different groups. We study how the variation of the number of clusters affects its performance in the light of two performance measurements: recommendation time and precision. We also analyze if this clustering is affected by the representation of text we use. All the techniques used in this study uses word-embeddings to represent the document. One of the main findings of this work is that using clustering we can improve the recommendation time in up to almost 30 times without affecting much off its initial accuracy. Another interesting finding is that the increment of the number of clusters is not directly translated into linear performance.

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.

Improving Scalability, Sparsity and Cold Start user Issues in Collaborative Tagging with Incremental Clustering and Trust

2019 ◽  
Vol 12 ◽  
Author(s):  
Latha Banda ◽  
Karan Singh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Web Sites ◽  
Cold Start ◽  
Incremental Clustering ◽  
Collaborative Tagging ◽  
Or Efficiency ◽  
Huge Data ◽  
Day By Day

: As web sites grows the complexity increases in many websites due to huge data. To maintain these data it is very difficult because there are many number of users are increasing day by day. As per information of many websites, there is very insufficient data to get the accuracy or efficiency of web sites. To improve the quality of websites, Recommender Systems are introduced. On the basis of these recommender systems, user gives the ratings to an item and then the reviews are generated for each an item so that the user might know the information of items relevant to his preferences. Here the RS is classified into content-based and collaborative filtering. Later tagging also included in this collaborative filtering. The main issues of Collaborative Filtering are Scalability, cold-start user and sparsity problems. We propose and explore the benefits of collaborative filtering based on tagging for sparseness, scalability and Cold start user issues.

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

scholarly journals Clustering Algorithms in Hybrid Recommender System on MovieLens Data

2014 ◽  
Vol 37 (1) ◽  
pp. 125-139 ◽  
Author(s):  
Urszula Kuzelewska
Keyword(s):  
Recommender System ◽  
Web Sites ◽  
Clustering Algorithms ◽  
Leisure Activities ◽  
Similarity Measures ◽  
Clustering Methods ◽  
Log Files ◽  
E Learning ◽  
On Line ◽  
Hybrid Recommender

AbstractDecisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files.The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function.

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