Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

Abstract

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user's internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.

Recommendation System with Biclustering

“…Apart from fusing the prediction ratings of two models, other kinds of fusions have also been proposed, for example the fusion in Ref. [34] is performed by combining different similarities instead of different predictions.…”

Multiple Constraint Hybrid Travel Route Recommendation Model Based on Collaborative Filtering Recommendation Algorithm

“…Based on the massive amount of information emerging from the development of the web, many scholars have studied recommender systems [11,12,13]. Liang and other scholars propose a balanced recommendation algorithm for teaching sports network based on trust relationship in response to the problems of low recommendation trust and poor recommendation resources in traditional sports network.…”