Personalized Recommendation
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2022 ◽  
Vol 12 (1) ◽  
pp. 0-0

Nowadays, in online social networks, there is an instantaneous extension of multimedia services and there are huge offers of video contents which has hindered users to acquire their interests. To solve these problem different personalized recommendation systems had been suggested. Although, all the personalized recommendation system which have been suggested are not efficient and they have significantly retarded the video recommendation process. So to solve this difficulty, context extractor based video recommendation system on cloud has been proposed in this paper. Further to this the system has server selection technique to handle the overload program and make it balanced. This paper explains the mechanism used to minimize network overhead and recommendation process is done by considering the context details of the users, it also uses rule based process and different algorithms used to achieve the objective. The videos will be stored in the cloud and through application videos will be dumped into cloud storage by reading, coping and storing process.


Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6758
Author(s):  
Xiujuan Wang ◽  
Yi Sui ◽  
Kangfeng Zheng ◽  
Yutong Shi ◽  
Siwei Cao

Based on the openness and accessibility of user data, personality recognition is widely used in personalized recommendation, intelligent medicine, natural language processing, and so on. Existing approaches usually adopt a single deep learning mechanism to extract personality information from user data, which leads to semantic loss to some extent. In addition, researchers encode scattered user posts in a sequential or hierarchical manner, ignoring the connection between posts and the unequal value of different posts to classification tasks. We propose a hierarchical hybrid model based on a self-attention mechanism, namely HMAttn-ECBiL, to fully excavate deep semantic information horizontally and vertically. Multiple modules composed of convolutional neural network and bi-directional long short-term memory encode different types of personality representations in a hierarchical and partitioned manner, which pays attention to the contribution of different words in posts and different posts to personality information and captures the dependencies between scattered posts. Moreover, the addition of a word embedding module effectively makes up for the original semantics filtered by a deep neural network. We verified the hybrid model on the MyPersonality dataset. The experimental results showed that the classification performance of the hybrid model exceeds the different model architectures and baseline models, and the average accuracy reached 72.01%.


2021 ◽  
Vol 13 (19) ◽  
pp. 11121
Author(s):  
Guohui Song ◽  
Yongbin Wang

At present, most news aggregation platforms use personalized recommendation technology to push information in China, which is likely to cause the phenomenon of information cocoons. In order to alleviate the occurrence of this phenomenon, this paper studies the issue of mainstream value information push from different perspectives, which can be used as a supplement for personalized recommendation technology to promote the diffusion of mainstream value information. First, we constructed an evolutionary game model to simulate the game process between news aggregation platforms and users. Through the results of evolutionary analysis, the news platform can be guided at a macro level to formulate mainstream value information push strategies by adjusting model parameters. Second, we conducted research on user behavior, and the results show that different user groups have different demands for mainstream value information. Third, we constructed two models from the perspective of user demands and platform revenue. Experiments show that user sensitivity to mainstream value information and platform evaluation factors are important for finding the number of mainstream information pushes on each page. Finally, we investigated the effect of the mainstream value information from Toutiao. The survey results are consistent with the viewpoints presented in this paper.


2021 ◽  
Vol 11 (18) ◽  
pp. 8664
Author(s):  
Huiying Jin ◽  
Pengcheng Zhang ◽  
Hai Dong ◽  
Mengqiao Shao ◽  
Yuelong Zhu

The rapid development of social networking platforms in recent years has made it possible for scholars to find partners who share similar research interests. Nevertheless, this task has become increasingly challenging with the dramatic increase in the number of scholar users over social networks. Scholar recommendation has recently become a hot topic. Thus, we propose a personalized scholar recommendation approach, Mul-RSR (Multi-dimensional features based Research Scholar Recommendation), which improves accuracy and interpretability. In this work, Mul-RSR aims to provide personalized recommendation for academic social platforms. Mul-RSR uses the Doc2Vec text model and the random walk algorithm to calculate textual similarity and social relevance to measure the correlation between scholars. It is able to recommend Top-N scholars for each scholar based on multi-layer perception and attention mechanism. To evaluate the proposed approach, we conduct a series of experiments based on public and self-collected ResearchGate datasets. The results demonstrate that our approach improves the recommendation hit rate, and the hit rate reaches 59.31% when the N value is 30. Through these evaluations, we show Mul-RSR can provide a more solid scientific decision-making basis and achieve a better recommendation effect.


2021 ◽  
Vol 11 (18) ◽  
pp. 8613
Author(s):  
Qinglong Li ◽  
Xinzhe Li ◽  
Byunghyun Lee ◽  
Jaekyeong Kim

As the e-commerce market grows worldwide, personalized recommendation services have become essential to users’ personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users’ purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user’s preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users’ purchasing decisions in personalized recommendation services. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company.


2021 ◽  
Author(s):  
Vito Walter Anelli ◽  
Tommaso Di Noia ◽  
Felice Antonio Merra

2021 ◽  
pp. 2141013
Author(s):  
N Zafar Ali Khan ◽  
R. Mahalakshmi

Product recommendation is an important functionality in online ecommerce systems. The goal of the recommendation system is to recommend products with has higher purchase success ratio. User profile, product purchase history etc. have been used in many works to provide high quality recommendations. Product reviews is one of the important source for personalized recommendation. Typical collaborative recommendation systems are built upon user rating on products. But in many cases, these rating information are inaccurate or not available. There is also a problem of biased reviews decreasing the accuracy of recommendation systems. This work proposes a aspect mining collaborative fusion based recommendation system considering both the implicit and explicit reviews. The sentiments about different aspects mined from reviews are translated to multi-dimensional ratings. These ratings are then fused with user profile and demographic attributes to improve the quality of recommendation. The proposed recommendation system has 3.79% lower RMSE, 4.51% lower MAE and 22% lower MRE compared to most recent collaborative filtering based recommendation system.


Author(s):  
Yang Yang ◽  
Yi Zhu ◽  
Yun Li

2021 ◽  
pp. 115825
Author(s):  
Yi Zhu ◽  
Xindong Wu ◽  
Jipeng Qiang ◽  
Yunhao Yuan ◽  
Yun Li

2021 ◽  
Vol 23 (3) ◽  
pp. 51-75
Author(s):  
Qinglong Li ◽  
◽  
Shibo Cui ◽  
Byunggyu Shin ◽  
Jaekyeong Kim

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