Abstract
Recommended system is beneficial to e-commerce sites, which provides customers with product information and recommendations; the recommendation system is currently widely used in many fields. In an era of information explosion, the key challenges of the recommender system is to obtain valid information from the tremendous amount of information and produce high quality recommendations. However, when facing the large mount of information, the traditional collaborative filtering algorithm usually obtains a high degree of sparseness, which ultimately lead to low accuracy recommendations. To tackle this issue, we propose a novel algorithm named Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor, which is based on the trust model and is combined with the user similarity. The novel algorithm takes into account the degree of interest overlap between the two users and results in a superior performance to the recommendation based on Trust Model in criteria of Precision, Recall, Diversity and Coverage. Additionally, the proposed model can effectively improve the efficiency of collaborative filtering algorithm and achieve high performance.
Highlights
The tremendous growth in information and number of global Internet users pose many key issues for recommender system to solve
This paper proposes a novel method: collaborative filtering recommendation based on Trust Model with Fused Similar Factor (TMFSF), which achieves higher precision and higher performance in making recommendations to users
The simulation experiment and contrast analysis are carried out based on the user-based collaborative filtering algorithm, collaborative filtering recommendation based on Trust Model and collaborative filtering recommendation based on Trust Model with Fused Similar Factor
Summary
The tremendous growth in information and number of global Internet users pose many key issues for recommender system to solve. The traditional collaborative filtering algorithm attempts to exploit the similarity between users who have the same interests and use the similarity to predict users’ interests. Chen et al [2] proposed an improved algorithm based on the optimization of user similarity was proposed by adding the equilibrium factor to the traditional Cosine Similarity algorithm. BK Patra et al [5] a similarity measure based on neighborhood CF was proposed It used the rating made by neighboring users. The methods mentioned above can improve the user similarity measure to a certain extent, they do not take into account the social trust in the similarity of users To target this problem, this paper proposes a novel method: collaborative filtering recommendation based on Trust Model with Fused Similar Factor (TMFSF), which achieves higher precision and higher performance in making recommendations to users
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