Research on big data mining based on improved parallel collaborative filtering algorithm

Zhu, Li; Li, Heng; Feng, Yan

doi:10.1007/s10586-018-2209-9

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Equation (8) shows the calculation method of the input gate. e function of the input gate is to selectively input historical data information and current state information.…”

Section: E Cognitive Ability Algorithm Based On Deep Learningmentioning

confidence: 99%

“…English interpreting itself is a tedious learning task, and the recommendation algorithm can help English interpreting learners achieve a more relaxed learning environment. e collaborative filtering (CF) algorithm is a relatively successful recommendation algorithm, which has been successfully applied in e-commerce and other fields [8,9]. It can make effective recommendations based on user behavior information and habit information.…”

Section: Introductionmentioning

confidence: 99%

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English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm

Ya-hong

2022

Mathematical Problems in Engineering

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In the context of globalization, English interpretation is the language hub for political and economic exchanges in different countries. English interpretation is a technology different from the English translation, which requires translation accuracy and real-time. Traditional English interpreting learning methods can only learn English grammar and some English language and cultural information. At the same time, traditional English interpreting learning methods are inefficient and cumbersome tasks. English interpreting participants also need to interpret accurately in real-time based on the speaker’s behavioral information and the cultural information contained in English. This puts forward more requirements for the learning of English interpreting learners. This research uses the collaborative filtering (CF) algorithm and the algorithm based on ConvLSTM cognitive ability to study the three characteristics of English grammar, behavioral information, and English cultural information in the English interpreting learning system. The CF algorithm can recommend effective English knowledge for English interpreting learners, and the ConvLSTM algorithm can interpret the effectiveness of this recommended knowledge. The research results show that the CF algorithm and the ConvLSTM algorithm have good performance in recommending and predicting the grammar, behavioral information, and cultural characteristics of the English interpreting learning system. The largest similarity index reached 0.97, and the smallest similarity index also reached 0.93. ConvLSTM can better predict the changing trend and data value size of English interpretation-related feature data, and the largest prediction error is only 2.48%.

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“…Equation (8) shows the calculation method of the input gate. e function of the input gate is to selectively input historical data information and current state information.…”

Section: E Cognitive Ability Algorithm Based On Deep Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm

Ya-hong

2022

Mathematical Problems in Engineering

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“…Most of the research experiments using collaborative filtering for music recommendation are used as a proof of the universality of the recommendation method proposed. e music industry often does not have enough rating data, and collaborative filtering can cause cold starts [22,23]. In recent years, some scholars use collaborative filtering combined with other information or mixed with other methods to build a music recommendation system, which has attracted more attention.…”

Section: Research Status Of Music Recommendation Systemmentioning

confidence: 99%

Automatic Recommendation of Online Music Tracks Based on Deep Learning

Gao

2022

Mathematical Problems in Engineering

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It is one of the main goals of personalized music recommendation system that how to accurately recommend the songs in line with users’ interests in the huge music library. In view of the above problems, this study proposes a personalized music recommendation method based on convolutional neural network. First, this study defines a training set containing potential musical characteristics and, combined with the depth of the belief network, design a music information prediction model and the research in the music-type classification method with different dimensions. Based on selecting four different kinds of music information better describing the underlying characteristics of 40D feature vector to every song music composition, the music feature set is constructed. Then, the CNN (convolutional neural network), which is widely used in audio field, is used as the music information prediction model, and its structural parameters are redesigned to complete the multidimensional music information prediction, which solves the cold start problem to some extent.

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“…It serves as a fundamental cornerstone for personalized services and decision-making in recommendation systems, targeted advertising, and precision marketing. The primary objective is to assist businesses in gaining a better understanding of their users and providing more personalized and accurate products and services.On the other hand, collaborative filtering algorithms are a commonly used category of algorithms in recommendation systems [2]. They analyze user behavior and preferences to discover similarities between users or items, thereby generating personalized recommendations for users.…”

Section: Introductionmentioning

confidence: 99%

Research on User Profile Combined with Collaborative Filtering Recommendation Algorithm for Intelligent Tourism

He,

Hua

2023

AJST

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In recent years, the online travel sector in the tourism industry has experienced significant growth and popularity due to the development and widespread adoption of internet technology and smart devices. However, despite these advancements, scenic spots have struggled to provide precise services to tourists, as the online marketplace is flooded with numerous and disorganized commodity resources, lacking standardized construction and systematic management. As a result, travelers find it challenging to access specialty goods that cater to their personalized needs. To address this issue, this paper proposes the use of user profiling and collaborative filtering recommendation algorithms to achieve personalized recommendations for specialty products in scenic spots. The general process of constructing user profiles for scenic spots and combining them with collaborative filtering algorithms to create an intelligent tourism recommendation system is outlined. The paper also highlights the current challenges faced by this system in practical applications and provides future research prospects to promote accurate services in tourist attractions.

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Research on big data mining based on improved parallel collaborative filtering algorithm

Cited by 10 publications

References 10 publications

English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm

English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm

Automatic Recommendation of Online Music Tracks Based on Deep Learning

Research on User Profile Combined with Collaborative Filtering Recommendation Algorithm for Intelligent Tourism

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