From: A systematic review and research perspective on recommender systems
References | Methodology | Features | Challenges |
---|---|---|---|
Castellano et al. [1] | Neuro-fuzzy | High accuracy | Model is not efficient for real-time applications |
Crespo et al. [2] | K-means clustering | Solves the problems related to electronic commerce and provides a good user experience | Model is not applicable on huge datasets |
Lin et al. [3] | GA, k-means, DT and BN | Increases profit in real-time applications along with product sale volumes | Lacks in operation efficiency |
Wang and Wu [4] | Association rule mining | Useful for real-time ubiquitous-learning applications | The model does not consider the learner’s behaviour |
García-Crespo et al. [5] | Fuzzy logic | High recommendation accuracy and scalability | Not applicable on large datasets |
Dong et al. [6] | Semantic similarity model | Solves the issue of service supply ecosystems in a software environment | Low overall performance |
Li et al. [7] | Fuzzy linguistic modelling | Good expert recommendations for knowledge management systems | Does not consider the user’s past preferences |
Lorenzi et al. [8] | Content-based filtering | This model can alleviate the sparsity issue of recommender systems | Low accuracy |
Huang et al. [9] | Rough set model | It enhances the accuracy of recommender systems | The model is complex for real-time applications |
Chen et al. [10] | Semantic web rule language | The model can efficiently extract the features of the dataset | The model is dependent on domain ontology data |
Mohanraj et al. [11] | Foraging Bees algorithm | A self-adaptive model with low computational time | Does not consider any change in user interests |
Hsu et al. [12] | Artificial bee colony algorithm | It has attained better accuracy and improved the execution time | The system is limited to the specific application only |
Gemmell et al. [13] | Linear-weighted hybrid approach | The model is highly effective and flexible for capitalizing on a robust relationship in different data dimensions | The model is complex and requires high memory |
Choi et al. [14] | Sequential pattern analysis | High accuracy and performance | This model cannot be used for large datasets |
Garibaldi et al. [15] | Fuzzy rule system | The model integrates variability with a conventional fuzzy inference system | Not suitable for sparse datasets |
Salehi [16] | Content-based filtering | Good F1 score, recall and precision values | Precision decreases with the increase of the size of the recommendation sets |
Aher and Lobo [17] | K-means and Association rule mining (ARM) | The model is capable of efficiently recommending online courses to new students | Does not consider specific choices of individual users |
Kardan and Ebrahimi [18] | ARM | This hybrid model delivers better efficiency when compared with individual models. It also considers implicit user information | The algorithm does not rank the outcome of the mining algorithm |
Chang et al. [19] | HFC, K-means clustering and KNN | Improved resource utilization in a cloud environment | This model suffers from hardware resource complications |
Lucas et al. [20] | Associative classification and Fuzzy logic | The model successfully alleviates problems such as gray sheep, cold-start, sparsity, and scalability issues | The model is not suitable for web mining applications |
Niu et al. [21] | Spectral clustering | The system attains better performance in terms of recall, precision and F1-score, and delivers a good user experience | Not applicable for low-level features |
Liu et al. [22] | Genetic Algorithm | A unique model was developed to save the travelling time of self-driven tourists | The model is platform-dependent, and the recommendations are not user-customizable |
Bakshi et al. [23] | Unsupervised learning, PSO | The model addresses the issues of scalability and sparse datasets | The selection of clusters is done arbitrarily |
Kim and Shim [24] | Probabilistic model | Good precision, recall and average hit rank | The model is complex |
Wang et al. [25] | GA and K-means | High prediction accuracy | The model is not suitable for real-time applications and sparse data |
Kolomvatsos et al. [26] | Stochastic decision making | It can efficiently handle multiple requests and save resources and time | The model is not suitable for real-time applications |
Gottschlich et al. [27] | Portfolio optimization algorithm | A unique model which considers the crowd’s wisdom to maximize a stock investor’s portfolio performance | It is a general model which does not consider specific user needs |
Torshizi et al. [28] | Fuzzy rule-based systems | A novel model useful for prostate treatment recommendations | The model strongly relies on medical data which lacks certainty |
Zahálka et al. [29] | Linear SVM model | An information-rich and interactive venue recommender model capable of connecting tourists | The model has low precision values for some datasets |
Sankar et al. [30] | Content-based filtering approach | A simple and accurate model for investment guidance | Hidden relationships among features may result in low accuracy |
Chen et al. [31] | Artificial immune system | It employs a new prediction formula to improve the recall, precision, and F1-scores | Suffers from data scalability, cold-start and memory-based issues |
Wu et al. [32] | Expectation maximization (EM) -based algorithm | Highly robust in fusing item recommendations for collaborative tagging systems | Low recommendation quality and not suitable for large datasets |
Yeh and Cheng [33] | Delphi panel and repertory grid techniques | The model is easy to use and solves the dimensionality problem and data sparsity issue | Not applicable for real-time applications |
Liao et al. [34] | Rough set association rule | The model considers dynamic user behaviour | High computation complexity and not fit for other applications |
Li et al. [35] | Association rule mining | The model can be applied to many different applications | Performance varies for different applications |
Wu et al. [36] | CTR and SMF | This model is simple and robust having high accuracy | Does not consider the social trust among users |
Adeniyi et al. [37] | KNN algorithm | A scalable model with a good precision rate and low computing load | Not applicable for real-time applications |
Rawat and Kankanhalli [38] | Image processing | A scalable system capable of learning offline and working in real-time | The system lacks personalization |
Yang et al. [39] | Hybrid gradient boosting | Suitable for real-time and large-scale recommender systems. It efficiently reduces false positive predictions | The model suffers from the cold-start problem and requires pre-clustering and feature engineering |
Lee et al. [40] | Collaborative ensemble learning | The model can handle dynamic user behaviour captured from mobile signals | Suffers from data sparsity and cold start issues |
Wei et al. [41] | SDAE | The model efficiently solves the cold start problem | Requires high computational resources and storage |
Li et al. [42] | Weighted linear regression | Low computational complexity and low prediction and modelling time for real-time applications | The model is complex to implement in real large-scale applications |
Mezei and Nikou [43] | Fuzzy optimization | Better accuracy and F1-score | Not scalable and cannot adopt other variants of fuzzy sets |
Ayata et al. [44] | SVM, RF, DT and KNN | The model can capture real-time user emotion in a dynamic environment | The model is susceptible to the failure of sensors |
Zhao et al. [45] | Multimodal network learning | This high-performance model is capable of handling real-world, large-scale datasets | It suffers from high computational costs and premature convergence |
Hammou et al. [46] | Matrix factorization and RF | The model reduced the cost functions by efficient learning of parameters to overcome the data sparsity problem | High computational cost and does not consider social contexts of users |
Zhao et al. [47] | ANNInit | A scalable model with high prediction accuracy and convergence efficiency | Not suitable for multi-dimension applications |
Bhaskaran and Santhi [48] | FA and K-mean clustering | High performance and accuracy with low error and computation time | Cannot be applied to other optimization methods |
Afolabi and Toivanen [59] | User-based approaches | A unique system where patients can share recommendations in real-time resulting in better self-care and decision making | Requires a smooth connection between different healthcare services |
He et al. [60] | Knowledge representation learning | A high-performance model which alleviates the data sparsity issue and cold start problem | The model is complex |
Han et al. [49] | BAS | The model is simple, portable and has high prediction accuracy with a fast convergence rate | BAS algorithm suffers from low search accuracy and limited exploration scope |
Kang et al. [50] | Hashmap | Solves the problem of data sparsity and overspecialization | High time complexity |
Ullah et al. [51] | RF and CNN | High efficacy and suitable for real-time product recommendations | It suffers from high time complexity |
Cai et al. [52] | MaoEA | High accuracy and applicability in diverse applications | The model lacks stability |
Esteban et al. [53] | GA | High performance and reliability for real-time course recommendations | The model lacks user personalization |
Mondal et al. [54] | Multilayer graph data model | It provides trust-based results with no requirement of external meta-data for calculating the trust factor | The model is only applicable to limited scenarios |
Dhelim et al. [55] | User-based approaches | The system can efficiently detect the meta-interest of the user and has a good recall and precision score for cold-start environments | It requires high computational power to decrease response time |
Bhalse et al. [56] | SVD, CS and collaborative filtering | It reduces the complexity by reducing the multi-dimensionality issue | Suffers from sparsity problem and low accuracy |
Ke et al. [57] | Reinforcement learning | High prediction accuracy and low entropy loss error | Not suitable for real-world problems |
Chen et al. [58] | FNN | The system delivers personalized suggestions by aggregating multiple user interest representations | Suffers from cold-start problem |