Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Ruihui Mu,Xiaoqin Zeng

doi:10.1155/2018/9617410

Ruihui Mu, Xiaoqin Zeng

Open Access

https://doi.org/10.1155/2018/9617410

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Abstract

To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.

Highlights

Due to information explosion, huge number of items areT present over web which makes it difficult for user to find appropriate item from available set of options
To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph
Collaborative Filtering (CF) is the most popular and Rwidely used approach for Recommender system (RS) which tries to analyze the similarity between two users or items by considering the corated items, which are commonly rated by both users

Summary

Introduction

Huge number of items areT present over web which makes it difficult for user to find appropriate item from available set of options. In [5], they proposed a collaborative filtering model that combines singularity and user’s interest over the target item on the basis of views diffusion processes to solve the problem of information expressed by other like-minded users. It has been success- overload by using the rating context information. In [6], fully applied in the industrial fields such as e-commerce they proposed content-based CF algorithm and improved the [1], online learning [2], and news media [3] The recommender explicit feedback data (such as rates) to make predictions or systems let users give ratings about a set of items such recommendations

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