An effective collaborative filtering algorithm based on user preference clustering

Lin, Yaojin; Lin, Menglei; Liu, Jinghua

doi:10.1007/s10489-015-0756-9

Cited by 81 publications

(29 citation statements)

References 38 publications

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“…To date, many researchers in the study have presented many metrics to evaluate the performance of recommendation systems. Generally speaking, the metrics for evaluating the recommendation system quality mainly include two categories: mean absolute error (MAE) and root mean square error (RMSE) [13].…”

Section: Discussionmentioning

confidence: 99%

“…erefore, there are many researchers who have studied these problems. A novel effective collaborative filtering algorithm based on user preference clustering is proposed to reduce the impact of the data sparsity [13]. And, due to the open nature of collaborative recommender systems, recommender systems cannot effectively prevent malicious users from injecting fake profile data into the ratings database; Zhang F introduced the social trust of the users into the recommender system and built the trust between them [14].…”

Section: Related Workmentioning

confidence: 99%

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A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

Tang

et al. 2019

Journal of Electrical and Computer Engineering

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Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

Tang

et al. 2019

Journal of Electrical and Computer Engineering

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show abstract

“…For example, Gan [13] proposed a method of estimating user similarity by establishing the relationship between user similarity and item similarity as a regression model. Further approaches include a proposal to supplement similarity by the addition of external data [18,19], and to alleviate sparsity through user clustering [20,21]. In this study, we also attempt to reduce sparsity by generating dense ratings from user log data for the most recent months and clustering items based on item similarity.…”

Section: Related Workmentioning

confidence: 99%

Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Lee

Kim

2019

Applied Sciences

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Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.

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“…Zhang et al proposed an effective way to improve the ability of finding the nearest neighbor and reliable for each active users. Their aim was to provide an effective model-based recommender system to solve the data sparsity problem [14]. In 2015, Park et.al proposed a fast collaborative filtering model using nearest neighbor graph which called RCF to reduce time complexity.…”

Section: Related Workmentioning

confidence: 99%

CUPCF: combining users preferences in collaborative filtering for better recommendation

Khalaji

Mohammadnejad

2019

SN Appl. Sci.

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How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict active users' tastes over a particular item. According to the cold start and data sparsity problems, these systems cannot predict and suggest particular items to users. In this paper, we introduce a new recommender system is able to find user preferences and based on it, provides the recommendations. Our proposed system called CUPCF is a combination of two similarity measures in collaborative filtering to solve the data sparsity problem and poor prediction (high prediction error rate) problems for better recommendation. The experimental results based on MovieLens dataset show that, combined with the preferences of the user's nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved. Furthermore, the results indicate the efficiency of CUPCF. The maximum improved error rate of the system is 15.5% and the maximum values of Accuracy, Precision and Recall of CUPCF are 0.91402, 0.91436 and 0.9974 respectively.

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An effective collaborative filtering algorithm based on user preference clustering

Cited by 81 publications

References 38 publications

A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

CUPCF: combining users preferences in collaborative filtering for better recommendation

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