A new user similarity model to improve the accuracy of collaborative filtering

Liu, Haifeng; Hu, Zheng; Mian, Ahmad Umair; Tian, Hui; Zhu, Xuzhen

doi:10.1016/j.knosys.2013.11.006

Cited by 544 publications

(251 citation statements)

References 27 publications

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“…In this way, the authors managed to reduce the calculation cost in data sets [7] Liu, Hu and Zhu developed an effective method with a new similarity model to improve the accuracy. This model showed context information on user rating and increased performance [13].…”

Section: Related Workmentioning

confidence: 99%

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Bakir

2018

Teh. vjesn.

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Nowadays, Collaborative Filtering (CF) is a widely used recommendation system. However, traditional CF techniques are harder to make fast and accurate suggestions due to changes in user preferences over time, the emergence of new products and the availability of too many users and too many products in the system. Therefore, it becomes more important to make suggestions that are both fast and take the changes in time into consideration. In the presented study, a new method for providing suggestions customized according to the users' preference and taste as they change over time was developed. By combining the time-dependent changes through the SVD (Singular Value Decomposition), a faster suggestion system was developed. Thus, an attempt was made to enhance product prediction success. In the present study all techniques on Netflix data and the results were compared. The results obtained on the accuracy of the predicted ratings were found out to be promising.

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Section: Related Workmentioning

confidence: 99%

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Bakir

2018

Teh. vjesn.

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“…In addition, it suggests a factorized version of the neighborhood model, which improves its computational complexity while retaining prediction accuracy. Liu et al [24] present a new user similarity model to improve the recommendation performance when only few ratings are available to calculate the similarities for each user. The model considers the local context information of user ratings, as well as the global preference of user behavior.…”

Section: Related Workmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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“…The recommendation system is a kind of decision-support system based on the customer's preference [16]. In recent years, it has been widely used in e-commerce sites to provide consumers with product purchasing advice [17,18].…”

Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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Abstract:With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer's feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

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A new user similarity model to improve the accuracy of collaborative filtering

Cited by 544 publications

References 27 publications

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Using Time Clusters for Following Users’ Shifts in Rating Practices

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

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