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Open Access Article
1 - Optimization of weighting-based approach to predict and deal with cold start of web recommender systems using cuckoo algorithm
reza molaee fard -
Open Access Article
2 - Review on Recommender System and Architecture
Mehrdad MollaNoroozi -
Open Access Article
3 - Presenting a novel method based on collaborative filtering for nearest neighbor detection in recommender systems
Mahdi Bazargani Zeinab HomayounpourRecommendation systems propose specific items to users based on their interests by analysis the user data. The main goal of this analysis is extraction of each user pattern to predict the interested items. One of the main well-known methods in recommender systems is col MoreRecommendation systems propose specific items to users based on their interests by analysis the user data. The main goal of this analysis is extraction of each user pattern to predict the interested items. One of the main well-known methods in recommender systems is collaborative filtering in which similarity measures are utilized to detect similar users to a new user. The challenging issues related to collaborative filtering are similarity and neighborhood detection. In this paper, nearest neighbor (NN) algorithm is used to detect similar neighbors to a new user. The proposed model, which is inspired by user-item method, the score of items is calculated based on a distance metric and the nearest neighbor is selected. In the presented work, we detect similar users using user-item matrix and the Euclidean distance. The proposed method is evaluated on Movielens dataset which includes 1682 items and evaluation metrics such as Accuracy, Precision, Recall, F1-measure, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are measured. MAE of the proposed method is 0.7351 which is less than Pearson and Cosine similarities, which demonstrates the superior performance of the proposed method in similarity detection and prediction. Manuscript profile -
Open Access Article
4 - A Trust-based Recommender System Using an Improved Particle Swarm Optimization Algorithm
Sajad Ahmadian Mohammad Hossein OlyaeeIntroduction: Recommender systems are intelligent tools to help users find their desired information among a large number of choices based on their previous preferences in a way faster than search engines. One of the main challenges in recommender systems is the sparsit MoreIntroduction: Recommender systems are intelligent tools to help users find their desired information among a large number of choices based on their previous preferences in a way faster than search engines. One of the main challenges in recommender systems is the sparsity of the user-item rating matrix. This means that users mainly tend to express their opinions about a few items, leading to a large portion of the user-item rating matrix being empty. Trust-based recommender systems aim to alleviate the sparsity problem using trust relationships between users. Trust relationships can be used to calculate similarity values between users and determine the nearest neighbors set for the target user. However, the efficiency of trust-based recommender systems depends on the correct selection of neighboring users for the target user based on the similarity values between users. Method: In this paper, a novel trust-based recommender system is proposed based on an improved particle swarm optimization algorithm. To this end, first, the similarity values between users are calculated based on the user-item rating matrix and trust relationships. Then, the improved particle swarm optimization algorithm is used to optimally weight the neighboring users of the target user. The main purpose of this algorithm is to assign an optimal weight to each user in the nearest neighbor set of the target user to predict the unknown items accurately. After the optimal weighting of neighboring users, unknown ratings are predicted for the target user. Results: The proposed method is evaluated on a standard dataset in terms of mean absolute error, root mean square error, and rate coverage metrics. Experimental results demonstrate the high efficiency of the proposed method compared to other methods. Discussion: We use the genetic algorithms operators and chaos-based asexual reproduction optimization algorithm to improve the original version of the particle swarm optimization algorithm. The genetic algorithms operators increase the exploration mechanism of the particle swarm optimization algorithm, leading to a decline in the probability of tapping into local optima. Moreover, the chaos-based asexual reproduction optimization algorithm is applied to the best solution to further search the area around the best solution. Manuscript profile -
Open Access Article
5 - Development of a multi-agent recommender system for intelligent shopping assistants
Ramazan Teimouri Yansari Mojtaba AjoudaniIntroduction Due to the increasing volume and services available on the web, tools such as recommender systems in websites and applications that can help users find information and services of interest can be provided. For this reason, suitable guidance and suggestions MoreIntroduction Due to the increasing volume and services available on the web, tools such as recommender systems in websites and applications that can help users find information and services of interest can be provided. For this reason, suitable guidance and suggestions for users in different choices, according to the user's priorities in different areas of a specific position, have been provided. Method Recommender systems are information systems that assist in the decision-making process by modeling the behavior of users in operational environments in ranking, comparing, selecting and choose items by users, narrowing the information search through high-quality and accurate recommendations. In this research, a multi-agent recommender system is proposed as an intelligent shopping assistant in the process of buying suitable offers. The proposed model is used to analyze the sales data set of a UK-based store containing 1,067,371 records of online sales data. Results By simulating the proposed model, the results of applying the model to the relevant data were analyzed. The proposed model in this research was simulated in MATLAB software version 2022 and the results of applying the proposed model on the data related to the sale of an online shopping were analyzed. According to the results, in this evaluation, the accuracy of the proposed model was 91.5% on average, compared to the neural network model, it was 86.41%, compared to the KNN model, 78.32%, compared to the SOM Ensembles model, 74.38%, compared to the Global Top-N model, 69.78%, compared to the Weighted item-based model, 72.31%, and compared to The Naïve Bayesian model has an accuracy of 59.68%, a higher accuracy in the right suggestion to users. Discussion In this research, while studying recommender systems, the challenges in this field were examined and multi-agent systems were used to provide suggestions and recommendations with high accuracy and quality in ranking, comparison, selection and preferences of users' items in the decision-making process in operational environments. By combining multi-agent systems, multi-agent recommender systems were proposed that can provide suitable recommendations as a purchasing assistant in the purchasing process. The results of applying the proposed model on the data related to the purchase history of the customers of an online shopping showed that the proposed model has a good efficiency in evaluating the parameters used in comparison with the common methods in this property field. Manuscript profile -
Open Access Article
6 - An algorithm for clustering of insurance products and users in a collaborative filtering-based insurance recommender system and evaluating its performance based on the insurance recommendation
Marzieh Amini Shirkoohi Mohammadreza YamaghaniIntroduction There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance be MoreIntroduction There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance because of various type of insurance and lack of enough information of insurance service. Choosing the proper insurance service always related to people personal and social features Method Prediction of customer’s insurance selection according to people personal and social property especially thier financial condition play vital role. On one hand Prediction of insurance type can help people who want to utilize insurance service. On the other hand this prediction can facilitate process of insurance for Insurers too. There are multiple important mechanisms and factors like customers clustring, analyze each class feature, detection of popular insurance in each class and using Collaborative filtering technique to offer best insurance that can influence on process of decision and selection the suitable insurance. Results The total precision value of the proposed method is 89.98% for joint insurances of similar users. Also, the total value of the F-measure of the proposed method for joint insurances between similar customers is 87.13%. Discussion Customer behavior can be predicted by available data of people’s personal and social features and type of insurance that they are chosen and rate of their satisfactions. K-means clustring algorithm and recommender systems Techniques like Collaborative filtering are two significant mechanisms to implement prediction of customer’s behaviors. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Manuscript profile -
Open Access Article
7 - Survey of Confidentiality and trust in recommender systems
Seyed Hossein HosseiniNazhad Morteza Abdi Reyhan -
Open Access Article
8 - Providing a Recommendation System for Recommending Articles to users using Data Mining Methods
Reza Molaee fard Payam Yarahmadi -
Open Access Article
9 - A New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Sama Jamalzehi Mohammad Bagher Menhaj -
Open Access Article
10 - Merging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems
Leily Sheugh Sasan H. Alizadeh -
Open Access Article
11 - Design an Intelligent Multi-agent Computer-aided System for Recommender Systems
Ramazan Teimouri Yansari Mojtaba Ajoudani Seyed Reza Mosayyebi -
Open Access Article
12 - Designing a Trust-Based Recommender System in Social Rating Networks
Maedeh Kiani Sarkaleh Fatemeh Khoshnood -
Open Access Article
13 - Devising a Profit-Aware Recommender System using Multi-Objective GA
Yaser Nemati Hossein Khademolhosseini -
Open Access Article
14 - Increasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms
Ali Pazahr -
Open Access Article
15 - Provide a video recommendation system using collaborative filtering and data mining methods
Reza Molaee Fard -
Open Access Article
16 - An Optimal Similarity Measure for Collaborative Filtering Using Firefly Algorithm
Fatemeh Shomalnasab Mehdi Sadeghzadeh Mansour Esmaeilpour -
Open Access Article
17 - A Novel Clustering Algorithm Based upon Learning Automata for Collaborative Filtering
Sara Taghipour Javad Akbari Torkestani Sara Nazari -
Open Access Article
18 - User Classification in a Personalized Recommender System using Learning Automata
Mansoureh Ghiasabadi Farahani Akbar Torkestani Mohsen Rahmani -
Open Access Article
19 - A Solution Towards to Detract Cold Start in Recommender Systems Dealing with Singular Value Decomposition
Keyvan Vahidy Rodpysh Seyed Javad Mirabedini Touraj Banirostam -
Open Access Article
20 - A Novel Method for Improving Cold Start Challenge in Recommender Systems through Users Demographics Information
Taravat Abedini Alireza Hedayati Ali Harounabadi -
Open Access Article
21 - Context-Aware Recommender Systems: A Review of the Structure Research
Seyed Javad Mirabedini Touraj Banirostam Keyvan Vahidy Rodpysh
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