A Jaccard base similarity measure to improve performance of CF based recommender systems

Ayub, Mubbashir; Ghazanfar, Mustansar Ali; Maqsood, Muazzam; Saleem, Asjad

doi:10.1109/icoin.2018.8343073

Cited by 30 publications

(17 citation statements)

References 7 publications

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“…After this, the researcher compared the performance of the proposed method with many traditional similarity measures. The recommendation results show that the proposed method performance is found well in terms of several evaluation metrics as compared to other traditional methods [38].…”

Section: Wherementioning

confidence: 79%

An Improved Product Recommendation Method for Collaborative Filtering

et al. 2020

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Collaborative filtering (CF) is the most commonly used technique for online recommendations. CF works by computing the interests of a user by gathering preferences or taste information of other users. In this technique, similar users or items are discovered by exploring the user-item rating matrix. Based on the computed similarity, a prediction is made for the unknown or new product. There are many similarity computation methods, such as the Pearson correlation coefficient (PCC), Jaccard, Mean square difference, Cosine, etc. however, the accuracy of product recommendations using these methods is not very promising. This work introduces an improved product recommendation method for collaborative filtering, which is based on the triangle similarity. However, the downside of triangle similarity is that it only considers the common ratings of users. The proposed similarity measure not only focuses on common ratings but also consider the ratings of those items that are not commonly rated from pairs of users. The obtained similarity is further complemented with the user rating preference (URP) behavior in giving rating preferences. To evaluate the accuracy, experiments are performed on the six commonly used datasets in the field of CF. Experimental results prove that the proposed similarity measure performs well as compared to the existing similarity measures. INDEX TERMS Collaborative filtering, recommender systems, triangle similarity, user preferences.

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Section: Wherementioning

confidence: 79%

An Improved Product Recommendation Method for Collaborative Filtering

et al. 2020

Self Cite

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“…Actually, the literature takes three major directions in dealing with the similarity measures. One direction tries to identify the limitations and drawbacks of the traditional similarity measures [7][8][9][10][11][12][13][14], as summarized in Table 1. This table lists the problem code, name, a brief description of each one, and their references.…”

Section: Related Workmentioning

confidence: 99%

“…So, they proposed variants of the Jaccard index, which considers all user ratings irrespective of whether they are common. Ayub et al [14] proposed a similarity measure based on the concept of the Jaccard index by introducing another argument to consider the average ratings of users. Schwarz et al [23] inversed the Euclidean distance to be a similarity measure for finding the coincidence between users.…”

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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“…It is the ratio of common items rated by the user to the total number of items rated by both the users. The formula to calculate Jaccard Similarity is given below [66].…”

Section: Jaccard Similaritymentioning

confidence: 99%

A review of movie recommendation system: Limitations, Survey and Challenges

Sunilkumar

2020

ELCVIA

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Recommendation System is a major area which is very popular and useful for people to take proper automated decisions. It is a method that helps user to find out the information which is beneficial to him/her from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. To overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have surveyed state-of-the-art methods of Content Based Filtering, Collaborative Filtering, Hybrid Approach and Deep Learning Based Methods for movie recommendation. We have also reviewed different similarity measures. Various companies like facebook which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored.

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A Jaccard base similarity measure to improve performance of CF based recommender systems

Cited by 30 publications

References 7 publications

An Improved Product Recommendation Method for Collaborative Filtering

An Improved Product Recommendation Method for Collaborative Filtering

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

A review of movie recommendation system: Limitations, Survey and Challenges

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