Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering

Chen, Jianrui; Zhao, Chenglin; Uliji,; Chen, Lifang

doi:10.1007/s40747-019-00123-5

Cited by 68 publications

(37 citation statements)

References 21 publications

(27 reference statements)

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“…Results of two-tailed t -tests are similar to one-tailed results for two datasets. Katarya and Verma [49] Priyankka et al [51] Chen et al [48] Pradeep and Bhaskar [47] Mohapatra et al [50] Our Approach RMSE values Zhou et al [37,52] Chen and others [48] Yu et al [37,53] Sarwar et al [37,55] Hinton [37,56] Papadakis et al [37,57] Sanandaj and Alizadeh [37] Siddiquee et al [13] Our Approach RMSE values Figure 5. Comparison of RMSE values with different studies for Movielens 1M dataset.…”

Section: Performance Evaluationmentioning

confidence: 99%

Evolutionary neural networks for improving the prediction performance of recommender systems

Seref¹,

Bostancı²,

Güzel³

2021

Turk J Elec Eng & Comp Sci

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Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving performance of them. In this study, prediction performances of neural networks were evaluated and their performances were improved using genetic algorithms. Performance of this study was compared with other studies, after that, superiority of this study was shown. While multilayer perceptron, generalized feed-forward network and CANFIS (Coactive Neuro Fuzzy Inference Systems) were used as a neural network algorithms, Movielens 100K and Movielens 1M datasets which are preferred in recommender systems studies mostly were used to train and test the system. Mean square error and root mean square error were employed as performance metrics. As a result, it was observed that genetic algorithm improves performance of neural networks and prediction performance of hybrid combination of neural networks and genetic algorithm is superior to prediction performance of recommender systems available in the literature.

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Section: Performance Evaluationmentioning

confidence: 99%

Evolutionary neural networks for improving the prediction performance of recommender systems

Seref¹,

Bostancı²,

Güzel³

2021

Turk J Elec Eng & Comp Sci

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“…The main limitation of collaborative filtering systems is that it suffers from limited data information expandability and the problem to a cold start [12]. Mansur et al [14] have observed common challenges across most collaborative filtering algorithms, including difficulty in providing recommendations to new users, limited trust in the conclusions made from small data, user data privacy, and sparsity of data [15] [16].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

Yeh¹,

Kashef²

2020

AIT

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A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as banking, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the usage of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be applied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced research on Blockchain and recommendation systems. We aim at building a secure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recommendation system with acceptable performance.

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“…In [25] [27] [26] [28], novel heterogeneous evolutionary clustering algorithms are presented. The goal of these algorithms is to gather users with similar interest into the same cluster and to help users find items that fit their personal tastes best.…”

Section: Evolutionary Computing In Recommender Systemsmentioning

confidence: 99%

“…In [28], a novel collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering is presented. Firstly, score matrix is pre-processed with normalization and dimension reduction.…”

Section: Evolutionary Computing In Recommender Systemsmentioning

confidence: 99%

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

Cardenas

Calisaya²,

Martinez-Salinas

et al. 2020

IJACSA

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In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

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Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering

Cited by 68 publications

References 21 publications

Evolutionary neural networks for improving the prediction performance of recommender systems

Evolutionary neural networks for improving the prediction performance of recommender systems

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

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