An Enhanced Prospective Jaccard Similarity Measure (PJSM) to Calculate the User Similarity Score Set for E-Commerce Recommender System

Mohana, H. S.; Suriakala, M.

doi:10.1007/978-981-15-5400-1_14

Cited by 4 publications

(1 citation statement)

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“…Improve e-shop online revenue by providing users with personalized recommendation [3][4][5]. So far, various types of data analysis tools have emerged one after another, among which the most successful and widely used method is the collaborative filtering recommendation algorithm [6,7]. The algorithm analyzes the interests and preferences of users [8,9] by obtaining explicit (user's rating of items) or implicit information [10] (tracking user behavior, such as purchase history, browsing data, movies watched and visit time, etc.)…”

Section: Introductionmentioning

confidence: 99%

Research on Concept Cluster and Data Filling Algorithm for Sparse Data

2023

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In order to reduce the impact of sparse data on the recommendation quality of the algorithm, this paper proposes a collaborative filtering recommendation algorithm that combines concept clustering and data filling. First, according to the formal background constructed based on the user-item rating matrix and pruning conditions, the object and attribute-induced concept clusters are obtained respectively, after that the target user's nearest neighbor candidate set is determined from the obtained concept clusters to reduce the noise users that affect the similarity measure. Secondly, the rating of unrated items was estimated according to the weights of users' interest in item attributes and the filling rules defined in the paper, then the data of user-item matrix was filled by the pre-estimation to reduce the sparsity of the data set and improve the recommendation accuracy of the algorithm. Finally, a differentiated similarity method is used to measure the similarity between the target user and the user's neighbor set, then predict the user's rating. The comparison experiments of the algorithm in this paper with 8 representative algorithms on two different datasets show that the proposed algorithm has good performance in both prediction and recommendation accuracy.

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