Abstract
The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.
Highlights
With the development of information technology, the data on the Internet has grown exponentially, and how to effectively provide relevant information to users in need is facing great challenges [1,2,3,4] in recent years
Based on the above research, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion (DMHR algorithm), which aims at the balance of user score distribution and the difficulty of multi-recommendation in recommendation system
Hybrid recommendation model based on deep emotion analysis and multi-source view fusion In view of the above discussion on the status quo of recommendation model research, this paper proposes a hybrid recommendation model based on deep emotion analysis of user reviews and cooperative fusion of multisource recommendation views, named DMHR
Summary
With the development of information technology, the data on the Internet has grown exponentially, and how to effectively provide relevant information to users in need is facing great challenges [1,2,3,4] in recent years To this end, various information sharing systems have been spawned, and online social networks are undoubtedly one of the most popular Internet products in the last decade [5,6,7], which provides the basic conditions for maintaining social relationships, such as discovering users with similar interests and hobbies, and acquiring information and knowledge shared by other users. There are usually more serious data sparse and cold start problems in social networks compared with the traditional recommendation algorithm, which brings great challenges to the research of social recommendation algorithms
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