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Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-b...
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... a better recommendation process with an increased the number of clusters as shown in Fig. 10, the HCSUC algorithm has obtained the best results for finding the set of feasible clusters of similarity users. It has better results . 1 30 5 10 3, 5 4 Exp. 2 90 10 30 3, 5, 7, 9 4, 8, 12 Exp. 3 120 15 60 3, 5 4, 8, 12 than PSO which reached up to 4%, better than ABO which reached up to 3%, and better than CSA which reached up to 5% ...Context 3
... better than CSA which reached up to 5% during iterations. In addition, the performance of the proposed algorithm has been improved ranging from 2% from the first iteration to 5% at the last iteration. By increasing the number of clusters, the HCSUC algorithm has gradually obtained better results when compared with other presented algorithms as in Fig. ...Context 4
... at the first number of iterations as seen in Fig. 11, PSO has achieved better results than ABO and HCSUC, it saturates after that. The CSA has achieved better results than PSO, ABO, and HCSUC. Overall, HCSUC has obtained better results than PSO which reached up to 10% with more iterations, better than ABO which reached up to 9%, and better than CSA which reached up to 8% with more ...Context 5
... proposed algorithm has improved by 12% during the number of iterations. Figure 12 illustrates the convergence curve of a fitness function for all algorithms in the case of 90 users grouped into 9 clusters to improve the top-8 recommendation process. ...Context 6
... Fig. 12, the PSO and standard CSA have obtained results close to each other's for the first number of iterations, but CSA has obtained better results than PSO with more iterations. Although the ABO has obtained better results than PSO and CSA, the proposed HCSUC algorithm has gradually decreased and reached better results than PSO within the ...Context 7
... Table 5, the HCSUC algorithm has obtained average results better than the other compared algorithms for enhancing the different top-N recommendations. Fig- ures 13 and 14 illustrate the minimization of an average fitness function for forming a feasible set of 3 and 5 clusters, respectively. ...Context 8
... was observed from Fig. 13 that by iterations, both PSO and ABO results are minimized, while the standard crow search algorithm has gained better results from both PSO and ABO from the start to the end of iterations. The proposed hybrid crow has obtained the best minimization results over other compared algorithms and has an improvement which reached up to 4% ...Context 9
... shown in Fig. 14, ABO has obtained the worst results, while for the first iterations, CSA and PSO have obtained results near to each other and at the end of iterations. Of all over the iterations, the HCSUC has the superiority of obtaining the best results with an improvement up to 5% when compared with other ...Context 10
... more illustrations, with 15 active users ðU act ¼ 15Þ, Figs. 15 and 16 show the MAE and RMSE for 3 and 5 clusters with top 4, 8, and 12 recommendations for each ...Context 11
... Fig. 15, the MAE of the proposed algorithm is minimized by 3% (i.e., in the case of 3 clusters with top 12 recommendations) when compared with PSO and CSA. In addition, it minimized by 7 and 4% (i.e., in the case of 3 clusters with top 8 recommendations) when compared with PSO and ABO, respectively. In the case of 3 clusters with top-4 ...Context 12
... to Fig. 16, the HCSUC algorithm has proven its efficiency in terms of RMSE where it has better results than PSO ranging from 2 to 7% for different clusters and recommendations. In addition, it has superiority when compared with ABO and standard CSA with the best results reached being up to 6 and 3% ...Context 13
... is compared against the conventional collaborative filtering. Table 6 shows the variation of MAE and RMSE for different numbers of randomly selected active users (e.g., 5 and 10) using all algorithms for recommending top-4 jokes. From Table 6, the result of CF has the worst values when compared with meta-heuristic algorithms, whereas Fig. 17, the CF technique has achieved the maximum MAE, while the proposed algorithm has obtained the minimum value for recommendation with different experiments. The HCSUC has achieved better results than CF within the range from 18 to 34%, better than PSO ranging from 12 to 16%, better than ABO, and CSA up to ...Context 14
... to the results of RMSE, as shown in Fig. 18, the proposed algorithm has obtained results better than other CF and other meta-heuristic algorithms. When compared with CF, the HCSUC has achieved improvements in the RMSE ranging from 15 to 29% along with Convergence curve for 90 users grouped into 3 clusters (top-N ¼ 12) Fig. 10 Convergence curve for 90 users grouped into 5 ...Context 15
... to the results of RMSE, as shown in Fig. 18, the proposed algorithm has obtained results better than other CF and other meta-heuristic algorithms. When compared with CF, the HCSUC has achieved improvements in the RMSE ranging from 15 to 29% along with Convergence curve for 90 users grouped into 3 clusters (top-N ¼ 12) Fig. 10 Convergence curve for 90 users grouped into 5 clusters (top-N ¼ 4) Fig. 11 Convergence curve for 90 users grouped into 7 clusters (top-N ¼ 12) Fig. 12 Convergence curve for 90 users grouped into 9 clusters (top-N ¼ 8) various experiments. When compared with other metaheuristics, the proposed algorithm has a minimized RMSE that reaches ...Context 16
... proposed algorithm has obtained results better than other CF and other meta-heuristic algorithms. When compared with CF, the HCSUC has achieved improvements in the RMSE ranging from 15 to 29% along with Convergence curve for 90 users grouped into 3 clusters (top-N ¼ 12) Fig. 10 Convergence curve for 90 users grouped into 5 clusters (top-N ¼ 4) Fig. 11 Convergence curve for 90 users grouped into 7 clusters (top-N ¼ 12) Fig. 12 Convergence curve for 90 users grouped into 9 clusters (top-N ¼ 8) various experiments. When compared with other metaheuristics, the proposed algorithm has a minimized RMSE that reaches up to 9% (in the case of ABO and CSA) and up to 12% (in the case of PSO). ...Context 17
... algorithms. When compared with CF, the HCSUC has achieved improvements in the RMSE ranging from 15 to 29% along with Convergence curve for 90 users grouped into 3 clusters (top-N ¼ 12) Fig. 10 Convergence curve for 90 users grouped into 5 clusters (top-N ¼ 4) Fig. 11 Convergence curve for 90 users grouped into 7 clusters (top-N ¼ 12) Fig. 12 Convergence curve for 90 users grouped into 9 clusters (top-N ¼ 8) various experiments. When compared with other metaheuristics, the proposed algorithm has a minimized RMSE that reaches up to 9% (in the case of ABO and CSA) and up to 12% (in the case of PSO). From all the above results, we can deduce that the proposed hybrid crow ...Similar publications
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... Content-Based (CB) filtering is a technique used to recommend items that share comparable characteristics to those that have captivated the interest of users. It recommends items based on the available information about them [35]. In other words, similar products are suggested based on ...
... Theoretical Background 35 transitioning from state x to y. The parameter , constrained within the range of 0 to 1, is used to regulate the impact of τ . ...
The fast growth of internet applications and services produces a
massive amount of information daily. With that rapid development of information, it becomes a challenging task for users to find content that satisfies their needs when they use online applications. Therefore, Recommendation Systems (RS) have become necessary for users. RS is a filtering technique that tries to reduce the available selections for users by finding the relevant items that satisfy their desires. Deep learning algorithms have significantly succeeded in several fields, including RS. Recently, many deep learning-based RSs have been proposed; they involve all the users in datasets to extract the latent representation of
input data to be used later for predicting the missing rates. Users have diverse preferences, making it challenging to create a single model that caters to all of them. This diversity results in recommendations that need to reflect individual user preferences accurately, where his work targeted bridging this gap as the primary objective. This dissertation proposed a new Optimized Clustering-based Denoising Autoencoder model (OCB-DAE), which trains multiple models based on users' preferences. The proposed model combined the Artificial Fish Swarm Algorithm (AFSA) with K-means algorithm to determine the best initial centroids for clustering the users based on their similarities, and each cluster trains a Denoising Autoencoder model (DAE) to ensure that users with similar interests train each model. OCB-DAE is applied to movies and food datasets to generate recommendations by utilizing the items features as side information. The proposed model was trained and tested over MovieLens 100K (ML-100K), MovieLens 1M (ML-1M), and Food.com datasets. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics were used to evaluate the performance of the proposed model. In ML-100K dataset, the RMSE and MAE were 0.5589 and 0.5050, respectively. Whereas in ML-1M dataset, the RMSE and MAE were 0.6742 and 0.5894, respectively. Regarding the Food dataset, the RMSE and MAE were 0.1922 and 0.1764, respectively. The proposed model
outperformed the related works in terms of RMSE with 29.7% and
14.4% using ML-100K and ML-1M datasets, respectively. In the food dataset, the proposed model outperformed non-clustered model in terms of RMSE by 24.3%. The results showed that training multiple models based on users' preferences reduced prediction errors and improved the recommendation systems' performance.
... Compared with the classical particle swarm optimization (PSO) and GA, CSA was found to be robust for standard BTFs. Clusters of similar users in recommendation systems were traced by a hybrid crow and a uniform crossover algorithm in [45]. The genetic crossover operator was built once again in CSA to increase the population diversity and to prevent the algorithm from trapping into a local optimum. ...
... GA, on the other hand, is a metaheuristic algorithm highly applicable in solving such a complex problem. Thus far, it has been considered to adjust the position of the crows after a flight with the help of only one genetic operatorcrossover [45,49]. The proposed GA-CSA hybrid fully integrates GA into CSA's initial group strategy, aiming to distribute the initial crows' population more uniformly and in a Fermentation 2024, 10, 12 6 of 23 more focused manner. ...
Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.
... The clustering methods are multiview, K-means, Top-N, Fuzzy C-means, Mapreduce and Ensemble. The next level is the SI which In [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], and [19], SI algorithms are used to optimize the position of the centroids in clustering approaches. In [8], Bee Swarm Optimization-MultiView (BSO-MV) generates better partitions in the selection of the initial medoids during multiview clustering for objective function. ...
... For K-means clustering, Artificial Bee Colony K-Means (ABC-KM) [9], K-Means Accelerated Particle Swarm Optimization (KM-APSO) [10], Improved K-Means Krill-Herd Algorithm (MMKHA) [11], Harris Hawk Optimization K-Means (HHO-KM) [12], and Crow Search Algorithm (CSA) [13] are used in optimizing the initial centroids' selection and updating their positions more effectively. In [14], CSA hybrid crow search and uniform crossover algorithm (HCSUC) is used in Top-N clustering to have better diversity of the search and help the algorithm to escape from the local minima trapping. For Fuzzy C-means clustering, Firefly Algorithm (FCM-FA) [16] is utilized for feature reduction while Moth Flame Optimization (MFOFCM) [15] has fast convergence for global optimum during clustering. ...
... CSA was combined with PSO to use both properties sufficiently to strengthen its exploration and exploitation processes [22]. An improved CSA was hybridized with a uniform crossover mechanism to enhance its exploration search capacity and convergence behavior [23]. Moreover, CSA has been fruitfully practiced for many optimization problems. ...
Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named Crow Search Algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length (fl) and awareness probability (AP) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values of fl and AP. A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others.
... El-Ashmawi et al. [14] had devised a novel algorithm for the detection of a feasible cluster set of similar users to boost the procedure of recommendation. Utilization of the genetic uniform crossover operator in the conventional Crow Search Algorithm (CSA) was able to increase the search's diversity as well as to aid the algorithm in avoiding capture in the local minima. ...
Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person’s mental stability. With the utilization of the sequential pattern mining algorithm, efficient extraction of frequent patterns from the database is achieved. A candidate sub-sequence generation-and-test method is adopted in conventional sequential mining algorithms like the Generalized Sequential Pattern Algorithm (GSP). However, since this approach will yield a huge candidate set, it is not ideal when a large amount of data is involved from the social media analysis. Since the data is composed of numerous features, all of which may not have any relation with one another, the utilization of feature selection helps remove unrelated features from the data with minimal information loss. In this work, Frequent Pattern (FP) mining operations will employ the Systolic tree. The systolic tree-based reconfigurable architecture will offer various benefits such as high throughput as well as cost-effective performance. The database’s frequently occurring item sets can be found by using the FP mining algorithms. Numerous research areas related to machine learning and data mining are fascinated by feature selection since it will enable the classifiers to be swift, more accurate, and cost-effective. Over the last ten years or so, there have been significant technological advancements in heuristic techniques. These techniques are beneficial because they improve the search procedure’s efficiency, albeit at the potential sacrifice of completeness claims. A new recommender system for mental illness detection was based on features selected using River Formation Dynamics (RFD), Particle Swarm Optimization (PSO), and hybrid RFD-PSO algorithm is proposed in this paper. The experiments use the depressive patient datasets for evaluation, and the results demonstrate the improved performance of the proposed technique.
... e corresponding information provider can accurately access the interests of different users, which could facilitate the stabilization of the customer base and control of industry trends, thus achieving a win-win situation for all parties. e advantage of recommendation systems over search engines and classified directories is that they use an active search strategy [12]. Specifically, a recommendation system can analyze the changing interests and needs of users by acquiring a large amount of historical behavioral data from them and provide them with the most interesting needs when they need to access information resources. ...
In recent years, with the continuous development of science and Internet technology, people’s lifestyles are changing dramatically, especially with the development of information technology, which has contributed to the transformation of digital libraries. As an essential information infrastructure and a new source of knowledge, digital libraries have brought great convenience to users. To be specific, with the widespread use of smart devices and internet of things technology, users are eager to be intelligent in their information needs while enjoying services, which makes the resource recommendation service of digital libraries increasingly important. In addition, as a provider of knowledge and information services, libraries should organically combine advanced information technology with existing resources to promote the construction of libraries in the information age. However, in the era of big data, users can only passively receive a large amount of information and services in the face of the ever-expanding mass of resources in digital libraries. In this context, libraries might only provide a single set of information resources and services, which cannot meet the individual needs of users and ultimately leads to inefficient allocation of resources and information. After all, users of digital libraries want to be better able to receive personalized recommendations for library resources through relevant technologies. At the same time, libraries are increasing their research and development efforts on algorithms and technologies for personalized recommendations. Also, with the explosive growth of the total amount of information worldwide, people are entering the information age. Massive amounts of data are constantly being generated, and the problem of information overload is becoming more and more serious. The sheer volume of this data and information increases the degree of difficulty in accessing the information people need. In this situation, it is necessary for digital libraries to dynamically analyze user behavior and interests while responding to user requests in a timely manner and accordingly take the initiative to recommend information resources and knowledge services that meet users’ individual needs. As a result, this study uses a deep belief network model for multimodal feature learning and designs a personalized recommendation system for library resources by fusing features from multiple modalities. Furthermore, this research implements the construction of a semantic user interest model and the design of a personalized recommendation algorithm to achieve an accurate description of user interest preferences and semantic personalized recommendation functions.
... The results reveal the proposed algorithm results in low Mean Absolute Error compared to the popular Pearson coefficient. Recent research by Ashami et al. [24] introduced a clustering-based approach to finding the best neighbor of the target user. The clustering is accomplished using the hybridization of the Crow Search and Uniform Crossover Algorithm and the Jester dataset is used as a test-bed for providing significantly less Mean Absolute Error. ...
Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to clients. Every one of these organizations must figure out how to improve the general client experience and meet every client's novel necessities, and big data helps with this cycle. Through the utilization and reviews of Big Data, travel industry organizations can study the inclinations of more modest portions of their intended interest group or even about people in some cases. In this paper, a Crow Search Optimization-based Hybrid Recommendation Model is proposed to get accurate suggestions based on clients' preferences. The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows’ assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, the performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58.5%, 27%, 24.5%, and 25.5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure respectively compared to similar algorithms.
... To overcome these shortcomings, many researchers developed new variants of the original CSA. El-Ashmawi et al. (2021) proposed a new hybrid algorithm by invoking the genetic uniform crossover mechanism in the original CSA to manage recommender systems. Sultana et al. (2020) applied CSA in conjunction with a response surface methodology for the reinforced concrete properties optimization. ...
One of the most common tasks of groundwater engineers is to estimate aquifer parameters from transient time-drawdown data which are measured during the execution of pumping tests. The classical method consists in the manual superimposition of the observed time-drawdown data over a type-curve, and the appreciation of the goodness-of-fit is left to the visual inspection of the engineer. The present paper proposes a computerized method based on a new hybrid algorithm named CSARao-1. It combines two recent metaheuristic algorithms: the crow search algorithm (CSA) and Rao-1 algorithm for the optimal aquifer parameters estimation, which inherits the advantages of both algorithms. The CSARao-1 hybrid algorithm along with CSA and Rao-1 algorithm was applied in conjunction with both the Theis solution to analyze time-drawdown datasets coming from confined aquifers systems and the Hantush and Jacob solution to analyze datasets coming from leaky aquifers systems. The proposed approach was coded in FORTRAN programming language and evaluated on fourteen time-drawdown datasets coming from different confined and leaky aquifers systems. The results obtained using CSARao-1 hybrid algorithm were compared to those obtained by applying CSA and Rao-1 algorithm separately, and to those recently published. Globally, the proposed CSARao-1 hybrid algorithm was found to exhibit more accuracy, better robustness and higher rate of convergence over the analyzed transient time-drawdown datasets.
... Recommender systems are a type of information filtering tool that aims to provide suggestions for items to be of use to the user. Such suggestions can relate to different decisionmaking processes, such as what users to connect to in a social network, what items to buy, which services to commit, what music to listen to, or what movie to watch [13]. RS can provide different users with various services to meet individual needs [14]. ...
A group recommender system (GRS) is a system that collectively recommends items to a group of users based on their preferences. The GRS and the individual RS challenge lies in a very small and incompleteness of user-item ratings. Such incompleteness resulted in the data sparsity problem. The issues of data sparsity in a group negatively affect the quality of recommendations to the group. It occurs due to the inefficient formation of groups, which usually involves individuals with sparse data in their user profiles. Most of the current studies focus on this issue after the formation of groups. However, this study focused before the group formation, based on the intuition that it will be more efficient if the data sparsity at the individual level is addressed before the group formation process takes place. Therefore, applying the approach through Linked Open Data (LOD) technology is proposed to ensure that the data sparsity issues can be overcome before the group formation process is implemented. We proposed a GRS-LOD model. The experimental evaluations relating to the prediction accuracy and recommendation relevancy of the proposed model were implemented on three aspects: comparison with the basic approach or baselines; comparison with the current approaches, and comparison in terms of group size and aggregation strategies. The aggregation strategies used were the Average (AV), Most Pleasure (MP), Average without Misery (AVM), and Least Misery (LM). The metrics for prediction accuracy were based on the RMSE and MAE, whereas for relevancy, precision, recall, and F1-score were considered. The results show that the prediction accuracy and relevancy of the developed model’s recommendations is better than the baseline study by adapting the Average (AV) strategy with the individual profile aggregation approach. Meanwhile, for the evaluation in terms of group size, the results show larger group size exhibits better prediction accuracy for the four used aggregation strategies. On the other hand, in terms of recommendation relevancy, the result shows that relevancy decreases with the increase in group size for the MP, AV and AVM strategies.
With the rapid development of the Internet in China and the further improvement of the penetration rate of mobile phones, the base of mobile phone netizens is increasing day by day. The news client has appeared on the media stage as a new thing. With the sharp increase in the amount of data, traditional collaborative filtering algorithms have problems of sparsity and cold start. Based on this, this study proposes an improved item-based collaborative filtering model. Firstly, descriptive statistical analysis is carried out on the user's historical data to grasp the basic situation of the user's reading news information. After that, a user-item matrix based on implicit scoring is constructed. The item similarity is calculated by the cosine similarity formula, and the top 5 pieces of information with high interest are selected to recommend to users. Use precision and recall to evaluate recommendation performance. In addition, the mode method and Top-N recommendation method are used to solve the problem of user cold start. Finally, the follow-up research points need to be paid attention to, so as to promote the further development of news client recommendation.KeywordsNews InformationIntelligent ApplicationTop-N Recommendation Method
