A Personalized Recommendation Algorithm with User Trust in Social Network

Dong, Yafei; Zhao, Chunhui; Cheng, Weijie; Li, Liang; Liu, Lin

doi:10.1007/978-981-10-2053-7_7

Cited by 1 publication

(1 citation statement)

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“…In an approach, researchers computed trust and distrust based on users' previous scores and statements of the explicit trust from the two personal and impersonal aspects, respectively [20]. Dong et al [36] presented a method to compute trust, in which the similarity between user ratings and their interests in each item has been combined to create a trust matrix among them. In another paper, a method that used a new confidence criterion was proposed to obtain implicit trust statements.…”

Section: Related Workmentioning

confidence: 99%

A Novel Trust Computation Method Based on User Ratings to Improve the Recommendation

Nozari

Koohi

Mahmodi

2020

IJE

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Today, the trust has turned into one of the most beneficial solutions to improve recommender systems, especially in the collaborative filtering methods. However, trust statements suffer from a number of shortcomings, including the trust statements sparsity, users' inability to express explicit trust for other users in most of the existing applications. To overcome these problems, this work presents a method for computing implicit trust based on user ratings, in which four influential factors including Similarity, Confidence, Analogous Opinion, and Distance are utilized to achieve trust. For computing users' similarity, Person Correlation Coefficient measure was applied. Confidence was computed through users' common in rated items. To compute users' analogous opinions, their ratings were evaluated from three aspects of their satisfaction, dissatisfaction, and indifference about the items. Euclidean distance was employed on users ratings for computing the distance. Finally, the factors were combined to reach the implicit trust. Moreover, fuzzy c-means clustering was applied to initially partition similar users for enhancing the performance positively. Finally, two MovieLens datasets of 100K and 1M have been used to evaluate this approach, and results have shown that the approach significantly increases Accuracy, Precision and Recall, compared to some other existing methods.

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Section: Related Workmentioning

confidence: 99%