Integrating Triangle and Jaccard similarities for recommendation

Sun, Shuang-Bo; Zhang, Zhiheng; Dong, Xin-Ling; Zhang, Heng‐Ru; Li, Tong-Jun; Zhang, Lin; Min, Fan

doi:10.1371/journal.pone.0183570

Cited by 61 publications

(48 citation statements)

References 38 publications

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“…The preference model is obtained by maximum likehood estimation. On a recent work, Sun et al proposed a new similarity measure of Triangle Multiplying Jaccard (TMJ) in [ 38 ], which combines triangle similarity and Jaccard similarly to improve recommendation accuracy. The TMJ is defined in Eq (8) .…”

Section: Related Workmentioning

confidence: 99%

An improved collaborative filtering method based on similarity

et al. 2018

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The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologies. In collaborative filtering, similarity calculation is the main issue. In order to improve the accuracy and quality of recommendations, we proposed an improved similarity model, which takes three impact factors of similarity into account to minimize the deviation of similarity calculation. Compared with the traditional similarity measure, the advantages of our proposed model are that it makes full use of rating data and solves the problem of co-rated items. To validate the efficiency of the proposed algorithm, experiments were performed on four datasets. Results show that the proposed method can effectively improve the preferences of the recommender system and it is suitable for the sparsity data.

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Section: Related Workmentioning

confidence: 99%

An improved collaborative filtering method based on similarity

et al. 2018

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“…As shown in the Definitions 2, which shows the existence of a and b in a vector space E, which is expressed in v or a ∩ b> 0. Similarly, the measurement of the Jaccard coefficient [3], dice coefficient [37], and cosine similarity that conceptually a measure involving the same vector, ie a, b, a ∩ b≠∈E [11]. Based on the implementation can be formulated as follows: The results of theorems 1,2 and 3 can be seen in figure 4.…”

Section: Description Framework Researchmentioning

confidence: 99%

An Approach Similarity to Customer Behavior in E-Metrics Ecosystem

Elveny¹

2020

Preprint

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Merchant is a business entity in cooperation with banks in the provision of payment. To the northSumatran region has 33 districts of the city and a population of 13,937,797 inhabitants. Each citydistrict has a variety of merchants that built on each corner or side of the road with a data sample of1,383 users. To search for users based on their behaviours be competitive merchant who takes thesimilarity of some objects. The similarity involves characterizing each object or expounds tour- tour ofthe object. How to measure the similarity to characterize each object resulting in the naming andweighting. The way it is made by comparing to what is common and what is different from the object.Results will be achieved in the form of a method of similarity to look for in a merchant's customerbehavior that competitive advantage

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“…Several similarity improvements are continuously being developed to increase the recommendations' accuracy. Among them are Bhattacharyya's similarity [16], the multi-level collaborative filtering similarity [27], the TMJ similarity [28], and the similarity integrating three impact factors, namely S 1 , S 2 , dan S 3 [2]. These four similarity algorithms only consider user rating value data explicitly to calculate the similarity between users.…”

Section: Similarity Algorithmmentioning

confidence: 99%

“…Their proposed similarity is an increase in the PCC similarity by taking into account the number of items co-rated on several levels. Sun et al [28] proposed a similarity algorithm by integrating the similarity of Triangle and Jaccard, known as TMJ similarity. Feng et al [2] proposed a new similarity algorithm by integrating three factors of similarity impact, namely S 1 ,S 2 , and S 3 .…”

Section: Introductionmentioning

confidence: 99%

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

Widiyaningtyas

Hidayah

Adji

2020

Preprint

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A recommendation system is a software used in the e-commerce field that provides recommendations for customers to choose the items they like. Several recommendation systems have been proposed; however, collaborative filtering is the most widely used approach. The main issue in collaborative filtering is how to implement a similarity algorithm that can improve performance in the recommendation system. Several similarity algorithms based on user rating value have been developed, and recently a similarity algorithm has been developed that combines the user rating value and the user behavior value. However, the existing research is still based only on a single user behavior value, which is the genre data. Therefore, we propose a new similarity algorithm that considers not only the genre data but also the user profile data (namely age, gender, occupation, and location). The new similarity we are proposing is called User Profile Correlation-based Similarity (UPCSim). The user profile correlation similarity was obtained by calculating the correlation coefficient between the user profile data and the user rating or behavior values. An experiment was done to compare the accuracy of the UPCSim algorithm with that of the previous algorithm. The experiment results show that the UPCSim algorithm can improve the recommendation performance MAE by 1.64% and RMSE by 1.4% compared to the previous algorithm.

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Integrating Triangle and Jaccard similarities for recommendation

Cited by 61 publications

References 38 publications

An improved collaborative filtering method based on similarity

An improved collaborative filtering method based on similarity

An Approach Similarity to Customer Behavior in E-Metrics Ecosystem

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

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