CBMR: An optimized MapReduce for item‐based collaborative filtering recommendation algorithm with empirical analysis

Li, Chenyang; He, Kejing

doi:10.1002/cpe.4092

Cited by 33 publications

(13 citation statements)

References 9 publications

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“…In [63] the authors propose a hybrid method using collaborative filter/item based to achieve a highly personalized product in the recommendation system. In [64] the authors propose an optimized MapReduce for item-based CF algorithm incorporated with empirical analysis to solve scalability and the processing efficiency of item based CF. • Privacy-Preserving: Privacy is considered one of the challenges found in the recommendation system applications.…”

Section: B Limitations Of Recommendation Systemmentioning

confidence: 99%

The Role of Trust to Enhance the Recommendation System Based on Social Network

Tharwat

Jacob

Fudzee

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Recommendation systems or recommender system (RSs) is one of the hottest topics nowadays, which is widely utilized to predict an item to the end-user based on his/her preferences primary. Recommendation systems applied in many areas mainly in commercial applications. This work aims to collect evidence of utilizing social network information between users to enhance the quality of traditional recommendation system. It provides an overview of traditional and modern approaches used by RSs such as collaborative filter (CF) approach, content-based (CB) approach, and hybrid filter approach. CF is one of the most famous traditional approaches in RSs, which is facing many limitations due to the lack of information available during a performance such as Cold start, Sparsity and Shilling attack. Additionally, this content focused on the role of incorporating a trust relationship from the social network to enhance the weaknesses of CF and achieve better quality in the recommendation process. Trust-aware Recommendation Systems (TaRSs) is a modern approach proposed to overcome the limitations of CF recommendation system in a social network. The trust relationship between users can boost and enhance CF limitations. Many researchers are focusing on trust in the recommendation system but fewer works are highlighting the role of trust in the recommendation system. In the end, limitations, and open issues of the current picture of the recommendation system come across.

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Section: B Limitations Of Recommendation Systemmentioning

confidence: 99%

The Role of Trust to Enhance the Recommendation System Based on Social Network

Tharwat

Jacob

Fudzee

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Shambour [33] used Euclidean distance to measure item-item similarity and showed such a method is better than traditional similarity approaches. Li et al [17] used the proportion of same users who rated items to measure item similarity. Moreover, many approaches seek to find similar items by incorporating user ratings [1,16,38] or images [5,10,22].…”

Section: Related Workmentioning

confidence: 99%

Quality-aware neural complementary item recommendation

Zhang¹,

Lu²,

Niu³

et al. 2018

Proceedings of the 12th ACM Conference on Recommender Systems

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Complementary item recommendation finds products that go well with one another (e.g., a camera and a specific lens). While complementary items are ubiquitous, the dimensions by which items go together can vary by both product and category, making it difficult to detect complementary items at scale. Moreover, in practice, user preferences for complementary items can be complex combinations of item quality and evidence of complementarity. Hence, we propose a new neural complementary recommender Encore that can jointly learn complementary item relationships and user preferences. Specifically, Encore (i) effectively combines and balances both stylistic and functional evidence of complementary items across item categories; (ii) naturally models item latent quality for complementary items through Bayesian inference of customer ratings; and (iii) builds a novel neural network model to learn the complex (non-linear) relationships between items for flexible and scalable complementary product recommendations. Through experiments over large Amazon datasets, we find that Encore effectively learns complementary item relationships, leading to an improvement in accuracy of 15.5% on average versus the next-best alternative.

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“…Recommender systems can be built through many approaches and have been successfully deployed in many businesses, such Amazon.com [5] and Netflix.com [6], social networks [7], and research papers [8]. Collaborative filtering algorithms have been widely used for recommendation systems [9]. Other technologies have also been applied to recommender systems, including Bayesian networks, clustering, and content-based methods.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Recommender System Using Locality Sensitive Hashing

Zhang

Fan

Wang

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Recommender systems are widely used for personalized recommendation in many business applications such as online shopping websites and social network platforms. However, with the tremendous growth of recommendation space (e.g., number of users, products, etc.), traditional systems suffer from time and space complexity issues and cannot make real-time recommendations when dealing with large-scale data. In this paper, we propose an efficient recommender system by incorporating the locality sensitive hashing (LSH) strategy. We show that LSH can approximately preserve similarities of data while significantly reducing data dimensions. We conduct experiments on synthetic and real-world datasets of various sizes and data types. The experiment results show that the proposed LSH-based system generally outperforms traditional item-based collaborative filtering in most cases in terms of statistical accuracy, decision support accuracy, and efficiency. This paper contributes to the fields of recommender systems and big data analytics by proposing a novel recommendation approach that can handle large-scale data efficiently.

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CBMR: An optimized MapReduce for item‐based collaborative filtering recommendation algorithm with empirical analysis

Cited by 33 publications

References 9 publications

The Role of Trust to Enhance the Recommendation System Based on Social Network

The Role of Trust to Enhance the Recommendation System Based on Social Network

Quality-aware neural complementary item recommendation

An Efficient Recommender System Using Locality Sensitive Hashing

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