Conventional Recommendation Algorithms Research Articles

An Empirical Study on Personalized Product Recommendation Based on Cross-Border E-Commerce Customer Data Analysis

Thanks to the rapid growth of cross-border e-commerce platforms, numerous cross-border items are now available to customers. Several serious issues with cross-border e-commerce platforms related to item promotion and consumer product screening have arisen. Particular importance should be placed on studying and implementing personalized recommendation systems based on international e-commerce. In light of the quick expansion of commodities, when making individualized suggestions, traditional recommendation algorithms have had to deal with issues such as scant data, a chilly start to the market, and trouble identifying user preferences. To automatically mine the implicit and latent relationships between users and objects in recommendation systems, this study employs deep learning with nonlinear learning capabilities, which resolves the challenges of user interest mining. The weaknesses of the existing global recommendation research are emphasized, the study of conventional recommendation algorithms mixed with deep learning technology is deep factorization machine (DeepFM) and neural matrix factorization (NeuMF) models. Both models excel in recommending implicit feedback data. The DeepFM model yields the lowest loss function values, while the NeuMF model outperforms the competing models in terms of HR@20 (a commonly used indicator to measure the recall rate) and loss functions. In summary, this research addresses critical issues in cross-border e-commerce by developing personalized recommendation systems and integrating deep learning with traditional recommendation algorithms to enhance global recommendations.

The Followee Recommendation Algorithm Based on Microblog User Interest and Characteristic

Different from conventional social networks and e-commerce systems, microblog community is unique for its low user activity, data sparsity and dynamic of user’s interests. Because of these challenges, conventional recommendation algorithms cannot get a satisfactory performance in microblog community. This paper proposes a microblog followee recommendation algorithm based on user interest degree and attribute characteristics after analyzing the structure of microblog recommendation, and this algorithm is mainly on the basis of user cf recommendation algorithm to recommend new followee to user. Firstly, group users through calculating users interest degree to followee; Secondly, get users’ preferences value to followee with Bayesian algorithm when they are with different attribute characteristics; Finally, calculate target user’s nearest neighbor set using a new optimized similarity degree method to form the recommendation list. Experiments show that this proposed algorithm enhances the effectiveness and accuracy of the nearest neighbor set, and the recommendation quality has significantly improvement compared with previous algorithm.