Measures of Similarity in Memory-Based Collaborative Filtering Recommender System

Stephen, S.C.; Xie, Hong; Rai, Shri

doi:10.1145/3092090.3092105

Cited by 18 publications

(3 citation statements)

References 26 publications

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“…They induced that the system performance is different for different sparsity levels. Moreover, Stephen et al [26] addressed sparse recommendation data and its effect on similarity calculations. They argued that finding two vectors with common ratings is even sparser.…”

Section: Related Workmentioning

confidence: 99%

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Al-Shamri

2022

Preprint

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Similarity measures are crucial for electing neighbors for the users of recommender systems. However, the massive amount of the processed data in such applications may hide the inner nature of the utilized similarity measures. This paper devotes lots of studies to three standard similarity measures based on many synthetic and real datasets. The aim is to uncover the hidden nature of such measures and conclude their suitability for the recommender systems under different scenarios. Moreover, we propose a novel similarity measure called the normalized sum of multiplications (NSM) and two different variants of it. For experimentation, we examine all measures at three levels; a toy example, synthetic datasets, and real-world datasets. The results show that sometimes Pearson correlation coefficient and cosine similarity exclude similar neighbors in favor of less valuable ones. The former measures the correlation direction, while the latter measures the angle. However, both direction and angle are not similarity but an indication to it and can have the same values for two far vectors. On the other hand, the proposed similarity measure constantly reveals the exact similarity and tracks the closest neighbors. The results prove its robustness and its very good predictive accuracy compared to the traditional ones.

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Section: Related Workmentioning

confidence: 99%

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Al-Shamri

2022

Preprint

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“…The former is divided into user-based CF and item-based CF. Memory-based CF [5] calculates the similarity between users or items, predicts the score of an item according to the score of the target user's nearest neighbor set, or find the neighbor set similar to the target item in the scored items, and predict the target score of the users on the target item based on the score value of each item in the neighbor set. When the number of users or items in the recommendation system increases rapidly, the CF recommendation algorithm based on the nearest neighbor needs to deal with the huge similarity calculation tasks, which results in the scalability problem of the recommendation system.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

Duan¹,

Wang²,

Han³

2021

KSII TIIS

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A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the coldstart problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

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“…Memory-and model-based techniques are commonly used to elucidate CF recommendations [3,[11][12][13][14][15]. Past studies have demonstrated the benefits of memory-based CF, wherein rating predictions are computed from the preferences of similar users via a rating matrix [12,[16][17][18][19]. Conversely, the model-based CF technique leverages a user-item rating matrix to initially build a predictive model using deep learning methods and then source the rating predictions from it [3,20].…”

Section: Introductionmentioning

confidence: 99%

Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

2020

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Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.

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Measures of Similarity in Memory-Based Collaborative Filtering Recommender System

Cited by 18 publications

References 26 publications

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

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