Статті в журналах: "Recommender Algorithm"

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Moses, Sharon J., and L. D. Dhinesh Babu. "Buyagain Grocery Recommender Algorithm for Online Shopping of Grocery and Gourmet Foods." International Journal of Web Services Research 15, no. 3 (July 2018): 1–17. http://dx.doi.org/10.4018/ijwsr.2018070101.

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Online shopping of grocery and gourmet products differ from other shopping activities due to its routine nature of buy-consume-buy. The existing recommendation algorithms of ecommerce websites are suitable only to render recommendation for products of one time purchase. So, in order to identify and recommend the products that users are likely to buy again and again, a novel recommender algorithm is proposed based on linguistic decision analysis model. The proposed buyagain recommender algorithm finds the semantic value of the user comments and computes the semantic value along with the user rating to render recommendation to the user. The efficiency of the buyagain recommender algorithm is evaluated using the grocery and gourmet dataset of amazon ecommerce websites. The end result proves that the algorithm accurately recommends the product that the user likes to purchase once again.

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2

Kavu, Tatenda D., Kudakwashe Dube, and Peter G. Raeth. "Holistic User Context-Aware Recommender Algorithm." Mathematical Problems in Engineering 2019 (September 29, 2019): 1–15. http://dx.doi.org/10.1155/2019/3965845.

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Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations. The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized. This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions. The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems. Web content mining and collaborative filtering approaches were used to develop a holistic user context-aware (HUC) algorithm. The algorithm was evaluated on a social network using online experimental evaluations. The algorithm demonstrated dynamism, novelty, and serendipity with an average of 84% novelty and 85% serendipity.

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3

Mali, Mahesh, Dhirendra Mishra, and M. Vijayalaxmi. "Benchmarking for Recommender System (MFRISE)." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (December 29, 2022): 146–56. http://dx.doi.org/10.17993/3ctic.2022.112.146-156.

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The advent of the internet age offers overwhelming choices of movies and shows to viewers which create need of comprehensive Recommendation Systems (RS). Recommendation System will suggest best content to viewers based on their choice using the methods of Information Retrieval, Data Mining and Machine Learning algorithms. The novel Multifaceted Recommendation System Engine (MFRISE) algorithm proposed in this paper will help the users to get personalized movie recommendations based on multi-clustering approach using user cluster and Movie cluster along with their interaction effect. This will add value to our existing parameters like user ratings and reviews. In real-world scenarios, recommenders have many non-functional requirements of technical nature. Evaluation of Multifaceted Recommendation System Engine must take these issues into account in order to produce good recommendations. The paper will show various technical evaluation parameters like RMSE, MAE and timings, which can be used to measure accuracy and speed of Recommender system. The benchmarking results also helpful for new recommendation algorithms. The paper has used MovieLens dataset for purpose of experimentation. The studied evaluation methods consider both quantitative and qualitative aspects of algorithm with many evaluation parameters like mean squared error (MSE), root mean squared error (RMSE), Test Time and Fit Time are calculated for each popular recommender algorithm (NMF, SVD, SVD++, SlopeOne, Co- Clustering) implementation. The study identifies the gaps and challenges faced by each above recommender algorithm. This study will also help researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.

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4

Huang, Jiaquan, Zhen Jia, and Peng Zuo. "Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space." Mathematical Modelling and Control 3, no. 1 (2023): 39–49. http://dx.doi.org/10.3934/mmc.2023004.

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<abstract>Collaborative filtering (CF) algorithm is one of the most widely used recommendation algorithms in recommender systems. However, there is a data sparsity problem in the traditional CF algorithm, which may reduce the recommended efficiency of recommender systems. This paper proposes an improved collaborative filtering personalized recommendation (ICF) algorithm, which can effectively improve the data sparsity problem by reducing item space. By using the k-means clustering method to secondarily extract the similarity information, ICF algorithm can obtain the similarity information of users more accurately, thus improving the accuracy of recommender systems. The experiments using MovieLens and Netflix data set show that the ICF algorithm has a significant improvement in the accuracy and quality of recommendation.</abstract>

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5

Mısır, Mustafa, and Michèle Sebag. "Alors: An algorithm recommender system." Artificial Intelligence 244 (March 2017): 291–314. http://dx.doi.org/10.1016/j.artint.2016.12.001.

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6

Cintia Ganesha Putri, Debby, Jenq-Shiou Leu, and Pavel Seda. "Design of an Unsupervised Machine Learning-Based Movie Recommender System." Symmetry 12, no. 2 (January 21, 2020): 185. http://dx.doi.org/10.3390/sym12020185.

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This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used average similarity, computational time, association rule with Apriori algorithm, and clustering performance evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies–Bouldin Index.

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7

Zhang, Heng-Ru, Fan Min, Xu He, and Yuan-Yuan Xu. "A Hybrid Recommender System Based on User-Recommender Interaction." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/145636.

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Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction. The recommender system accepts user request, recommendsNitems to the user, and records user choice. If some of these items favor the user, she will select one to browse and continue to use recommender system, until none of the recommended items favors her. Second, we propose a hybrid recommender system combining random andk-nearest neighbor algorithms. Third, we redefine the recall and diversity metrics based on the new scenario to evaluate the recommender system. Experiments results on the well-known MovieLens dataset show that the hybrid algorithm is more effective than nonhybrid ones.

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8

Kumar Ojha, Rajesh, and Dr Bhagirathi Nayak. "Application of Machine Learning in Collaborative Filtering Recommender Systems." International Journal of Engineering & Technology 7, no. 4.38 (December 3, 2018): 213. http://dx.doi.org/10.14419/ijet.v7i4.38.24445.

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Recommender systems are one of the important methodologies in machine learning technologies, which is using in current business scenario. This article proposes a book recommender system using deep learning technique and k-Nearest Neighbors (k-NN) classification. Deep learning technique is one of the most effective techniques in the field of recommender systems. Recommender systems are intelligent systems in Machine Learning that can make difference from other algorithms. This article considers application of Machine Learning Technology and we present an approach based a recommender system. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based book recommender system. We analyze the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the projected algorithm is more precise over the existing algorithm, it also consumes less time and reliable than the existing methods.

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9

Li, Wen-Jun, Yuan-Yuan Xu, Qiang Dong, Jun-Lin Zhou, and Yan Fu. "TaDb: A time-aware diffusion-based recommender algorithm." International Journal of Modern Physics C 26, no. 09 (June 22, 2015): 1550102. http://dx.doi.org/10.1142/s0129183115501028.

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Traditional recommender algorithms usually employ the early and recent records indiscriminately, which overlooks the change of user interests over time. In this paper, we show that the interests of a user remain stable in a short-term interval and drift during a long-term period. Based on this observation, we propose a time-aware diffusion-based (TaDb) recommender algorithm, which assigns different temporal weights to the leading links existing before the target user's collection and the following links appearing after that in the diffusion process. Experiments on four real datasets, Netflix, MovieLens, FriendFeed and Delicious show that TaDb algorithm significantly improves the prediction accuracy compared with the algorithms not considering temporal effects.

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10

Gelvez Garcia, Nancy Yaneth, Jesús Gil-Ruíz, and Jhon Fredy Bayona-Navarro. "Optimization of Recommender Systems Using Particle Swarms." Ingeniería 28, Suppl (February 28, 2023): e19925. http://dx.doi.org/10.14483/23448393.19925.

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Background: Recommender systems are one of the most widely used technologies by electronic businesses and internet applications as part of their strategies to improve customer experiences and boost sales. Recommender systems aim to suggest content based on its characteristics and on user preferences. The best recommender systems are able to deliver recommendations in the shortest possible time and with the least possible number of errors, which is challenging when working with large volumes of data. Method: This article presents a novel technique to optimize recommender systems using particle swarm algorithms. The objective of the selected genetic algorithm is to find the best hyperparameters that minimize the difference between the expected values and those obtained by the recommender system. Results: The algorithm demonstrates viability given the results obtained, highlighting its simple implementation and the minimal and easily attainable computational resources necessary for its execution. Conclusions: It was possible to develop an algorithm using the most convenient properties of particle swarms in order to optimize recommender systems, thus achieving the ideal behavior for its implementation in the proposed scenario.

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11

Rohani, Vala Ali, Zarinah Mohd Kasirun, Sameer Kumar, and Shahaboddin Shamshirband. "An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/123726.

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Abundance of information in recent years has become a serious challenge for web users. Recommender systems (RSs) have been often utilized to alleviate this issue. RSs prune large information spaces to recommend the most relevant items to users by considering their preferences. Nonetheless, in situations where users or items have few opinions, the recommendations cannot be made properly. This notable shortcoming in practical RSs is called cold-start problem. In the present study, we propose a novel approach to address this problem by incorporating social networking features. Coined as enhanced content-based algorithm using social networking (ECSN), the proposed algorithm considers the submitted ratings of faculty mates and friends besides user’s own preferences. The effectiveness of ECSN algorithm was evaluated by implementing it in MyExpert, a newly designed academic social network (ASN) for academics in Malaysia. Real feedbacks from live interactions of MyExpert users with the recommended items are recorded for 12 consecutive weeks in which four different algorithms, namely, random, collaborative, content-based, and ECSN were applied every three weeks. The empirical results show significant performance of ECSN in mitigating the cold-start problem besides improving the prediction accuracy of recommendations when compared with other studied recommender algorithms.

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12

Lahlou, Fatima Zahra, Houda Benbrahim, and Ismail Kassou. "Review Aware Recommender System." International Journal of Distributed Artificial Intelligence 10, no. 2 (July 2018): 28–50. http://dx.doi.org/10.4018/ijdai.2018070102.

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Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.

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13

Iqbal, Misbah, Mustansar Ali Ghazanfar, Asma Sattar, Muazzam Maqsood, Salabat Khan, Irfan Mehmood, and Sung Wook Baik. "Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm." IEEE Access 7 (2019): 24719–37. http://dx.doi.org/10.1109/access.2019.2897003.

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14

Karimi, Hassan A., Monir H. Sharker, and Duangduen Roongpiboonsopit. "Geocoding Recommender: An Algorithm to Recommend Optimal Online Geocoding Services for Applications." Transactions in GIS 15, no. 6 (November 22, 2011): 869–86. http://dx.doi.org/10.1111/j.1467-9671.2011.01293.x.

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15

Song, Rui Ping, Bo Wang, Guo Ming Huang, Qi Dong Liu, Rong Jing Hu, and Rui Sheng Zhang. "A Hybrid Recommender Algorithm Based on an Improved Similarity Method." Applied Mechanics and Materials 475-476 (December 2013): 978–82. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.978.

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Recommendation systems have achieved widespread success in E-commerce nowadays. There are several evaluation metrics for recommender systems, such as accuracy, diversity, computational efficiency and coverage. Accuracy is one of the most important measurement criteria. In this paper, to improve accuracy, we proposed a hybrid recommender algorithm by an improved similarity method (ISM), combining demographic recommendation techniques and user-based collaborative filtering (CF) algorithms. Experiments were performed to compare the present approach with the other classical similarity measures based on the MovieLens dataset. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values show the superiority of the proposed algorithm.

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16

Lin, Qiuzhen, Xiaozhou Wang, Bishan Hu, Lijia Ma, Fei Chen, Jianqiang Li, and Carlos A. Coello Coello. "Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation." Complexity 2018 (November 12, 2018): 1–18. http://dx.doi.org/10.1155/2018/1716352.

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Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.

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17

Zhou, Xun, Jing He, Guangyan Huang, and Yanchun Zhang. "Scalable approximating SVD algorithm for recommender systems." Web Intelligence and Agent Systems: An International Journal 12, no. 4 (2014): 359–73. http://dx.doi.org/10.3233/wia-140303.

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18

Zhang, Peng, Xiaoyu Song, Leyang Xue, and Ke Gu. "A new recommender algorithm on signed networks." Physica A: Statistical Mechanics and its Applications 520 (April 2019): 317–21. http://dx.doi.org/10.1016/j.physa.2019.01.054.

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19

Sulieman, Dalia, Maria Malek, Hubert Kadima, and Dominique Laurent. "Toward Social-Semantic Recommender Systems." International Journal of Information Systems and Social Change 7, no. 1 (January 2016): 1–30. http://dx.doi.org/10.4018/ijissc.2016010101.

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In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

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20

Zhang, Yang, Hua Shen, and Guo Shun Zhou. "NINU: An Incremental User-Based Algorithm for Data Sparsity Recommender Systems." Applied Mechanics and Materials 201-202 (October 2012): 428–32. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.428.

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Collaborative Filtering (CF) algorithms are widely used in recommender systems to deal with information overload. However, with the rapid growth in the amount of information and the number of visitors to web sites in recent years, CF researchers are facing challenges with improving the quality of recommendations for users with sparse data and improving the scalability of the CF algorithms. To address these issues, an incremental user-based algorithm combined with item-based approach is proposed in this paper. By using N-nearest users and N-nearest items in the prediction generation, the algorithm requires an O(N) space for storing necessary similarities for the online prediction computation and at the same time gets improvement of scalability. The experiments suggest that the incremental user-based algorithm provides better quality than the best available classic Pearson correlation-based CF algorithms when the data set is sparse.

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21

Chen, Jinpeng, Yu Liu, and Deyi Li. "Enhancing Recommender Diversity Using Gaussian Cloud Transformation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no. 04 (August 2015): 521–44. http://dx.doi.org/10.1142/s0218488515500233.

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The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

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22

Sun, Ping, Zheng Yu Li, Zi Yang Han, and Feng Ying Wang. "An Overview of Collaborative Filtering Recommendation Algorithm." Advanced Materials Research 756-759 (September 2013): 3899–903. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3899.

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Recommendation algorithm is the most core and key point in recommender systems, and plays a decisive role in type and performance evaluation. At present collaborative filtering recommendation not only is the most widely useful and successful recommend technology, but also is a promotion for the study of the whole recommender systems. The research on the recommender systems is coming into a focus and critical problem at home and abroad. Firstly, the latest development and research in the collaborative filtering recommendation algorithm are introduced. Secondly, the primary idea and difficulties faced with the algorithm are explained in detail. Some classical solutions are used to deal with the problems such as data sparseness, cold start and augmentability. Thirdly, the particular evaluation method of the algorithm is put forward and the developments of collaborative filtering algorithm are prospected.

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23

Sharon Moses J. and Dhinesh Babu L. D. "Genre Familiarity Correlation-Based Recommender Algorithm for New User Cold Start Problem." International Journal of Intelligent Information Technologies 17, no. 3 (July 2021): 30–49. http://dx.doi.org/10.4018/ijiit.2021070103.

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The advancement of web services paved the way to the accumulation of a tremendous amount of information into the world wide web. The huge pile of information makes it hard for the user to get the required information at the right time. Therefore, to get the right item, recommender systems are emphasized. Recommender algorithms generally act on the user information to render recommendations. In this scenario, when a new user enters the system, it fails in rendering recommendation due to unavailability of user information, resulting in a new user problem. So, in this paper, a movie recommender algorithm is constructed to address the prevailing new user cold start problem by utilizing only movie genres. Unlike other techniques, in the proposed work, familiarity of each movie genre is considered to compute the genre significance value. Based on genre significance value, genre similarity is correlated to render recommendations to a new user. The evaluation of the proposed recommender algorithm on real-world datasets shows that the algorithm performs better than the other similar approaches.

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R. J. Kuo, R. J. Kuo, and Zhen Wu R. J. Kuo. "Applying Evolutionary-based User Characteristic Clustering and Matrix Factorization to Collaborative Filtering for Recommender Systems." 網際網路技術學刊 23, no. 4 (July 2022): 693–708. http://dx.doi.org/10.53106/160792642022072304005.

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In recent years, with the rise of numerous Internet service industries, recommender systems have been widely used as never before. Users can easily obtain the information, products or services they need from the Internet, and businesses can also increase additional revenue through the recommender system. However, in today’s recommender system, the data scale is very large, and the sparsity of the scoring data seriously affects the quality of the recommendation. Thus, this study intends to propose a recommendation algorithm based on evolutionary algorithm, which combines user characteristic clustering and matrix factorization. In addition, the exponential ranking selection technology is employed for evolutionary algorithm. The experiment result shows that the proposed algorithm can obtain better result in terms of four indicators, mean square error, precision, recall, and F score for two benchmark datasets.

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Sun, Jinyang, Baisong Liu, Hao Ren, and Weiming Huang. "NCGAN:A neural adversarial collaborative filtering for recommender system." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 2915–23. http://dx.doi.org/10.3233/jifs-210123.

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The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users’ non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users’ ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators.

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Sharma, Bharti, Adeel Hashmi, Charu Gupta, Osamah Ibrahim Khalaf, Ghaida Muttashar Abdulsahib, and Malakeh Muhyiddeen Itani. "Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System." Symmetry 14, no. 4 (April 11, 2022): 793. http://dx.doi.org/10.3390/sym14040793.

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Recommendation systems suggest relevant items to a user based on the similarity between users or between items. In a collaborative filtering approach for generating recommendations, there is a symmetry between the users. That is, if user A has similar interests with user B, then an item liked by B can be recommended to A and vice versa. To provide optimal and fast recommendations, a recommender system may generate and keep clusters of existing users/items. In this research work, a hybrid sparrow clustered (HSC) recommender system is developed, and is applied to the MovieLens dataset to demonstrate its effectiveness and efficiency. The proposed method (HSC) is also compared to other methods, and the results are compared. Precision, mean absolute error, recall, and accuracy metrics were used to figure out how well the movie recommender system worked for the HSC collaborative movie recommender system. The results of the experiment on the MovieLens dataset show that the proposed method is quite promising when it comes to scalability, performance, and personalized movie recommendations.

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Dehdarirad, Hossein, Javad Ghazimirsaeid, and Ammar Jalalimanesh. "Scholarly publication venue recommender systems." Data Technologies and Applications 54, no. 2 (March 17, 2020): 169–91. http://dx.doi.org/10.1108/dta-08-2019-0135.

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PurposeThe purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.Design/methodology/approachTo identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.FindingsA total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.Originality/valueGiven that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.

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28

Shruti, Taware, Yadav Krushna, and Khatri Pavan. "Gift-Me : Personalized Gift Recommender System." INSIST 3, no. 1 (April 20, 2018): 143. http://dx.doi.org/10.23960/ins.v3i1.143.

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Nowadays most of the india market is moving towards an online world with an adroit to have an maximum market scope .So we are connecting the business of gift article to online world with the evolved versions of current algorithm of recommendation systems.Ecommerce is an online site where the sale or purchase of goods are ordered electronically.The available ecommerce system have some issues with the recommendation so we are collaborating the multiple algorithm to increase the product sale and convinient in user interaction

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Al-Safi, Jehan, and Cihan Kaleli. "Item Genre-Based Users Similarity Measure for Recommender Systems." Applied Sciences 11, no. 13 (June 30, 2021): 6108. http://dx.doi.org/10.3390/app11136108.

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A technique employed by recommendation systems is collaborative filtering, which predicts the item ratings and recommends the items that may be interesting to the user. Naturally, users have diverse opinions, and only trusting user ratings of products may produce inaccurate recommendations. Therefore, it is essential to offer a new similarity measure that enhances recommendation accuracy, even for customers who only leave a few ratings. Thus, this article proposes an algorithm for user similarity measures that exploit item genre information to make more accurate recommendations. This algorithm measures the relationship between users using item genre information, discovers the active user’s nearest neighbors in each genre, and finds the final nearest neighbors list who can share with them the same preference in a genre. Finally, it predicts the active-user rating of items using a definite prediction procedure. To measure the accuracy, we propose new evaluation criteria: the rating level and reliability among users, according to rating level. We implement the proposed method on real datasets. The empirical results clarify that the proposed algorithm produces a predicted rating accuracy, rating level, and reliability between users, which are better than many existing collaborative filtering algorithms.

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Kang, Changwan, and Seungbae Cho. "Automatic recommender algorithm of reviewers using machine learning." Korean Data Analysis Society 22, no. 6 (December 30, 2020): 2405–12. http://dx.doi.org/10.37727/jkdas.2020.22.6.2405.

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Shakil, Syed Ubaid, Mohammed Talha Alam, Shahab Saquib Sohail, Imran Khan Saifi, Tabish Mufti, Asfia Aziz, and Md Tabrez Nafis. "The Impact of Randomized Algorithm over Recommender System." Procedia Computer Science 194 (2021): 218–23. http://dx.doi.org/10.1016/j.procs.2021.10.076.

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Akhtarzada, Ali, Cristian S. Calude, and John Hosking. "A Multi-Criteria Metric Algorithm for Recommender Systems." Fundamenta Informaticae 110, no. 1-4 (2011): 1–11. http://dx.doi.org/10.3233/fi-2011-524.

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Yuan, Weiwei, and Donghai Guan. "OPTIMIZED TRUST-AWARE RECOMMENDER SYSTEM USING GENETIC ALGORITHM." Neural Network World 27, no. 1 (2017): 77–94. http://dx.doi.org/10.14311/nnw.2017.27.004.

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Kavu, Tatenda, Kudakwashe Dube, and Peter Raeth. "Erratum to “Holistic User Context-Aware Recommender Algorithm”." Mathematical Problems in Engineering 2020 (November 30, 2020): 1. http://dx.doi.org/10.1155/2020/4706185.

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Kaur, Sandeep, and Mini Ahuja. "Genetic algorithm based efficient social network Recommender System." International Journal of Computer Trends and Technology 36, no. 4 (June 25, 2016): 219–24. http://dx.doi.org/10.14445/22312803/ijctt-v36p138.

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Alhijawi, Bushra, and Yousef Kilani. "A collaborative filtering recommender system using genetic algorithm." Information Processing & Management 57, no. 6 (November 2020): 102310. http://dx.doi.org/10.1016/j.ipm.2020.102310.

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Huang, Zhen Hua, Dong Wang, and Sheng Li Sun. "Efficient Mining of Skyrank Items in Recommender Systems." Advanced Materials Research 472-475 (February 2012): 3450–54. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3450.

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Mining of skyrank items has recently received a lot of attention in recommender system community. Literature [3] presents an efficient algorithm ZHYX to produce the skyrank items in one single subspace. However, in multi-user environments, recommender systems generally receive multiple subspace skyrank queries simultaneously. Hence, in this paper, we propose the first efficient sound and complete algorithm, i.e. AMMSSI(Algorithm for Mining Multiple Subsapce Skyrank Items), to markedly reduce the total response time. The detailed theoretical analyses and extensive experiments demonstrate that our proposed algorithm is both efficient and effective.

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Walia, Prof Ranjanroop. "Online Recommender System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 30, 2021): 2569–77. http://dx.doi.org/10.22214/ijraset.2021.36424.

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As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user. Recommender system (RS) has emerged as a major research interest that Aims to help users to find items online by providing suggestions that Closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval.

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Bai, Yun, Suling Jia, Shuangzhe Wang, and Binkai Tan. "Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network." Information 11, no. 3 (March 23, 2020): 171. http://dx.doi.org/10.3390/info11030171.

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Inferring customers’ preferences and recommending suitable products is a challenging task for companies, although recommender systems are constantly evolving. Loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. To this end, the aim of this study is to explore whether customer loyalty can improve the accuracy of the recommender system. Two algorithms based on complex networks are proposed: a recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and a recommendation algorithm based on single vertex set network and DeepWalk (SVDW). In both algorithms, loyalty is taken as an attribute of the customer, and the relationship between customers and products is abstracted into the network topology. During the random walk among nodes in the network, product recommendations for customers are completed. Taking a real estate group in Malaysia as an example, the experimental results verify that customer loyalty can indeed improve the accuracy of the recommender system. We can also conclude that companies are more effective at recommending customers with moderate loyalty levels.

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Bahrkazemi, Maryam, and Maryam Mohammadi. "A strategy to estimate the optimal low-rank in incremental SVD-based algorithms for recommender systems." Intelligent Data Analysis 26, no. 2 (March 14, 2022): 447–67. http://dx.doi.org/10.3233/ida-205733.

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Recommender systems apply machine learning and data mining techniques for filtering unseen information, and they can provide an opportunity to predict whether a user would be interested in a given item. The main types of recommender systems are collaborative filtering (CF) and content-based filtering, which suffer from scalability and data sparsity resulting in poor quality recommendations and reduced coverage. There are two incremental algorithms based on Singular Value Decomposition (SVD) with high scalability for recommender systems which are named the incremental SVD algorithm and incremental Approximating the Singular Value Decomposition (ApproSVD) algorithm. In both mentioned methods, the estimated value of rank for approximating the recommender systems’ data matrix is chosen experimentally in the related literature. In this paper, we investigate the role of singular values for estimating a more reliable amount of rank in the mentioned dimensionality reduction techniques to improve the recommender systems’ performance. In other words, we offered a strategy for choosing the optimal rank that approximates the data matrix more accurately in incremental algorithms with the help of singular values. The numerical results illustrate that the suggested strategy improves the accuracy of the recommendations and run times of both algorithms when employs for Movielens, Netflix, and Jester dataset.

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Timothy Ogbeye, Olatunji, Felix Ola Aranuwa, Oluwafemi Oriola, Alaba Olu Akingbesote, and Ayokunle Olalekan Ige. "Data Mining-based Real-Time User-centric Recommender System for Nigerian Tourism Industry." International Journal of Engineering and Applied Computer Science 04, no. 04 (May 22, 2022): 23–28. http://dx.doi.org/10.24032/ijeacs/0404/009.

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The tourism information system in Nigeria is not novel. What is novel is the need to develop reliable real-time recommender systems that can adequately aid tourists in their decisions. Several researchers have proposed various models. However, there are still issues about the applicability, effectiveness, efficiency, and reliability of the existing recommenders in the Nigerian tourism sector. This work is aimed at developing an improved model for real-time tourism recommender in Nigeria based on a data mining model. The objectives include the development of a data mining model for real-time reliable user-centric tourism recommendation and evaluation of the recommender system. To achieve these, a supervised machine learning-based classifier is modelled. The classifier system is evaluated using four thousand (4,000) datasets acquired from online and physical Nigerian tourism sources. Nine machine learning algorithms are compared during the testing process based on accuracy and other standard performance metrics. Experimental results show that the PART algorithm outperforms all other algorithms with an accuracy of 91.65%, F-Measure of 0.917, true positive rate of 0.913, the false-positive rate of 0.029, and the precision of 0.917, and recall of 0.917. In terms of efficiency, it also records the least time-to-model of 0.02 seconds. The rules generated from this algorithm are incorporated into the design of a prototype to test the recommender. The usefulness and efficiency scores based on test cases involving 20 participants prove that the recommender system would be a veritable tool for tourism in Nigeria.

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Zeng, Wei, Ya Fan, Bao Zhuo Zhou, and Qing Xian Wang. "A Recommendation Algorithm from the Object Perspective." Applied Mechanics and Materials 687-691 (November 2014): 2664–67. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2664.

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The purpose of designing recommender systems is to help individual users find relevant information. However, many recommender systems have been facing the challenges of finding niche objects, which users may like but difficult to find due to the lack of sufficient data. In this paper, we propose a recommendation algorithm which takes a niche object as input and outputs a list of users who may be interested it. By this approach, every niche object can be recommended at least one time. Further analysis indicates that those niche objects are usually collected by active users and the owners who are very similar to each other. Therefore, this work has outlined the significant relevance with the challenge, the Long Tail problem, and provided a different perspective to solve it in the field of information filtering.

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Yang, Dan, Jing Zhang, Sifeng Wang, and XueDong Zhang. "A Time-Aware CNN-Based Personalized Recommender System." Complexity 2019 (December 18, 2019): 1–11. http://dx.doi.org/10.1155/2019/9476981.

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Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.

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Mustika, Hani Febri, and Aina Musdholifah. "Book Recommender System Using Genetic Algorithm and Association Rule Mining." Computer Engineering and Applications Journal 8, no. 2 (June 11, 2019): 85–92. http://dx.doi.org/10.18495/comengapp.v8i2.305.

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Recommender system aims to provide on something that likely most suitable and attractive for users. Many researches on the book recommender system for library have already been done. One of them used association rule mining. However, the system was not optimal in providing recommendations that appropriate to the user's preferences and achieving the goal of recommender system. This research proposed a book recommender system for the library that optimizes association rule mining using genetic algorithm. Data used in this research has taken from Yogyakarta City Library during 2015 until 2016. The experimental results of the association rule mining study show that 0.01 for the greatest value of minimum support and 0.4359 for the average confidence value due to a lot of data and uneven distribution of data. Furthermore, other results are 0.499471 for the average of Laplace value, 30.7527 for the average of lift value and 1.91534252 for the average of conviction value, which those values indicate that rules have good enough level of confidence, quite interesting and dependent which indicates existing relation between antecedent and consequent. Optimization using genetic algorithm requires longer execution time, but it was able to produce book recommendations better than only using association rule mining. In Addition, the system got 77.5% for achieving the goal of recommender system, namely relevance, novelty, serendipity and increasing recommendation diversity.

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Kaur, Lovedeep, and Naveen Kumari. "A Review on User Recommendation System Based Upon Semantic Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (November 30, 2017): 35. http://dx.doi.org/10.23956/ijarcsse.v7i11.465.

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Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.

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Kaur, Lovedeep, and Naveen Kumari. "A Research on user Recommendation System Based upon Semantic Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (November 30, 2017): 72. http://dx.doi.org/10.23956/ijarcsse.v7i11.471.

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Анотація:

Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.

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De Croon, Robin, Leen Van Houdt, Nyi Nyi Htun, Gregor Štiglic, Vero Vanden Abeele, and Katrien Verbert. "Health Recommender Systems: Systematic Review." Journal of Medical Internet Research 23, no. 6 (June 29, 2021): e18035. http://dx.doi.org/10.2196/18035.

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Background Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. Methods We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Results Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. Conclusions There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

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ZENG, WEI, MING-SHENG SHANG, QIAN-MING ZHANG, LINYUAN LÜ, and TAO ZHOU. "CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?" International Journal of Modern Physics C 21, no. 10 (October 2010): 1217–27. http://dx.doi.org/10.1142/s0129183110015786.

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Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

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Puthiya Parambath, Shameem A., and Sanjay Chawla. "Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations." Data Mining and Knowledge Discovery 34, no. 5 (August 3, 2020): 1560–88. http://dx.doi.org/10.1007/s10618-020-00708-6.

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Abstract Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.

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Sokolov, Mikhail. "Towards a Sociology of Suspicion: A Theory of Recommendational Relations with Applications to the Academic World." Sotsiologicheskoe Obozrenie / Russian Sociological Review 19, no. 1 (2020): 106–38. http://dx.doi.org/10.17323/1728-192x-2020-1-106-138.

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The article explores a distinct social form — recommendational relations — in which an agent (a recommender) serves for another (a recipient) as a source of information on a third one (a recommendee). Our vocabulary of suspicion suggests that in a situation like that, a recipient may fall a victim of collusion between the recommender and the recommendee. The readiness of the recommendee to trust the recommendation depends on relations in the triad and, specifically, on (1) the moral distances between them; (2) the recommender’s awareness of being a source of information on the recommendee; (3) the recommender’s preoccupation with other roles; (4) the possibilities of the recipient’s retaliation, and (5) the presence or absence of conditions for cooperation between the recommender and the recommendee. The character of distances between the agents (physical, cultural, or moral) determines which mechanisms of generating trust the recipient is most likely to rely on. It is further argued that some conditions on which a recipient may rely on from a recommender involve the latter’s externalization of their thinking processes and the leaving of material traces of the decision-making algorithm, as such traces may serve as a basis for the recommender’s retaliation. It is further argued that the degree of externalization is responsible for the overall dynamics of the signal system towards inflation (the decline of a particular signal’s “purchasing power” without the decline of its information contents) or devaluation (the decline of a signal’s ability to mark possession of certain qualities). Empirically, the article relies on the yields of a comparative study of academic markets, symbols of academic status, and the application of formal performance measures in five countries.

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