scholarly journals Hybrid recommendation system based on collaborative filtering and fuzzy numbers

2012 ◽  
Author(s):  
Miguel A. G. Pinto ◽  
Ricardo Tanscheit ◽  
Marley Vellasco
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Fuzzy Numbers ◽  
Hybrid Recommendation

scholarly journals New Similarity of Triangular Fuzzy Number and Its Application

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xixiang Zhang ◽  
Weimin Ma ◽  
Liping Chen
Keyword(s):  
Collaborative Filtering ◽  
Fuzzy Number ◽  
Recommendation System ◽  
Fuzzy Numbers ◽  
Comprehensive Evaluation ◽  
Cloud Model ◽  
Triangular Fuzzy Number ◽  
New Method ◽  
Triangular Fuzzy Numbers

The similarity of triangular fuzzy numbers is an important metric for application of it. There exist several approaches to measure similarity of triangular fuzzy numbers. However, some of them are opt to be large. To make the similarity well distributed, a new method SIAM (Shape’s Indifferent Area and Midpoint) to measure triangular fuzzy number is put forward, which takes the shape’s indifferent area and midpoint of two triangular fuzzy numbers into consideration. Comparison with other similarity measurements shows the effectiveness of the proposed method. Then, it is applied to collaborative filtering recommendation to measure users’ similarity. A collaborative filtering case is used to illustrate users’ similarity based on cloud model and triangular fuzzy number; the result indicates that users’ similarity based on triangular fuzzy number can obtain better discrimination. Finally, a simulated collaborative filtering recommendation system is developed which uses cloud model and triangular fuzzy number to express users’ comprehensive evaluation on items, and result shows that the accuracy of collaborative filtering recommendation based on triangular fuzzy number is higher.

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

2020 ◽  
Vol 19 (02) ◽  
pp. 385-412 ◽  
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Least Square ◽  
Data Sparsity ◽  
Filtering Method ◽  
Sparsity Problem ◽  
Hybrid Collaborative Filtering ◽  
User Ratings ◽  
Hybrid Recommendation

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

scholarly journals A Hybrid Recommendation System for Video Games: Combining Content-based & Collaborative Filtering

2021 ◽  
Vol 9 (9) ◽  
pp. 1647-1653
Author(s):  
Aditya Manikantan
Keyword(s):  
Video Games ◽  
Collaborative Filtering ◽  
Video Game ◽  
Recommendation System ◽  
Data Representation ◽  
Cosine Similarity ◽  
Novel Approach ◽  
Hybrid Recommender ◽  
Implicit Data ◽  
Hybrid Recommendation

Abstract: Recommending video games can be trickier than movies. When it comes to selecting a video game, many factors are involved such as its genre, platform on which it’s played, duration of main and side quests, and more. However, recommending games based on just these features won’t suffice as a person who, for example, enjoys a certain genre of game can equally enjoy a vastly different genre. Therefore, a scoring mechanism is required which takes into account both, features of a game (contentbased filtering) and also studies the buying patterns of people playing a particular game (collaborative filtering). In this paper I have proposed a way to take into account both content-based and collaborative filtering into the final recommendation. I have used cosine similarity to quantify the similarity between the features of games. Along with this, I have employed a Deep fullyconnected AutoEncoder (DAE) to generalize the implicit data representation of an user’s buying patterns. Finally, I present a novel approach to combine the scores of these filtering techniques in such a way that it gives equal weightage to both. In other words, they both have equal influence over the final list of the top 10 games recommended to the user. Keywords: Hybrid Recommender, Collaborative filtering, Content-based filtering, Cosine similarity, AutoEncoder.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

2021 ◽  
Vol 13 (13) ◽  
pp. 7156
Author(s):  
Kyoung Jun Lee ◽  
Yu Jeong Hwangbo ◽  
Baek Jeong ◽  
Ji Woong Yoo ◽  
Kyung Yang Park
Keyword(s):  
Collaborative Filtering ◽  
High Performance ◽  
Recommendation System ◽  
Small And Medium Enterprises ◽  
Personal Information ◽  
Business Environment ◽  
Share Data ◽  
Artificial Intelligence Technology ◽  
Medium Enterprises ◽  
Start Ups

Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.

Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

2021 ◽  
Vol 25 (4) ◽  
pp. 1013-1029
Author(s):  
Zeeshan Zeeshan ◽  
Qurat ul Ain ◽  
Uzair Aslam Bhatti ◽  
Waqar Hussain Memon ◽  
Sajid Ali ◽  
...  
Keyword(s):  
Recommendation System ◽  
Sources Of Information ◽  
Multiple Features ◽  
Recommendation Algorithms ◽  
Label Correlations ◽  
Feature Based ◽  
Multicriteria Model ◽  
Implicit And Explicit ◽  
Hybrid Recommendation ◽  
Different Sources

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

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