Quantum-Inspired Recommendation System with Threshold Proportion Interception

SPIN ◽  
2021 ◽  
Author(s):  
Meng Qiao ◽  
Zheng Shan ◽  
Junchao Wang ◽  
Huihui Sun ◽  
Fudong Liu
Keyword(s):  
Time Complexity ◽  
Numerical Experiments ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Industrial Applications ◽  
Constant Time ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Industrial Environments

Modern recommendation systems leverage historical behavior information to generate precise recommendation results for users. However, when the data scale of users and items is large, it is difficult to generate recommendation results in time. Tang proposed a quantum-inspired recommendation algorithm, which could solve the recommendation problem in constant time complexity. However, Tang’s approach is based on a set of assumptions which rely heavily on some empirical parameters. The time complexity for calculating parameters is high. Thus, this approach cannot be directly applied in industrial applications. In this paper, we propose a method, namely, Quantum-inspired Recommendation system with threshold Proportion Interception (QRPI), which is based on the quantum-inspired recommendation system and more suitable for industrial environments. Compared with the existing widely used recommendation algorithms, we show through numerical experiments that our solution can achieve almost the same performance with better efficiency.

scholarly journals SIS dynamic network model based on product semantics from the perspective of infectious disease dynamics

2021 ◽  
Vol 235 ◽  
pp. 03035
Author(s):  
jiaojiao Lv ◽  
yingsi Zhao
Keyword(s):  
Network Model ◽  
Purchase Intention ◽  
Recommendation System ◽  
Optimal Algorithm ◽  
Dynamic Network ◽  
Distribution Model ◽  
Recommendation Algorithm ◽  
Model Based ◽  
Dynamic Network Model ◽  
Recommendation Algorithms

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.

Research on a Theatre Recommendation System

2014 ◽  
Vol 989-994 ◽  
pp. 4775-4779
Author(s):  
Yu Long Li ◽  
Ying Li ◽  
Wei Jiang ◽  
Zhi Zhou
Keyword(s):  
Bipartite Graph ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Recommendation Algorithm ◽  
Graph Data ◽  
Data Standardization ◽  
Similarity Calculation

Nowadays the recommendation system has been widely used, especially in the field of e-commerce, SNS, music, etc. On the basis of recommendation systems which are widely used, the paper puts forward a theatre recommendation algorithm which is more suitable in the field of theatre. In order to achieve the recommendation of theatre, the paper uses a series of steps, including weight, bipartite graph, data standardization, similarity calculation. After using this algorithm, some theatres will be recommended according to recommendation level. The results of recommendation are more reasonable, effective and satisfied.

Constructing Recommendation Systems for Effective Health Messages Using Content, Collaborative, and Hybrid Algorithms

2015 ◽  
Vol 659 (1) ◽  
pp. 290-306 ◽  
Author(s):  
Joseph N. Cappella ◽  
Sijia Yang ◽  
Sungkyoung Lee
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Theory Development ◽  
Health Messages ◽  
Recommendation Algorithms ◽  
Public Health Messages ◽  
Message Testing ◽  
Conventional Models ◽  
Content Similarity ◽  
Empirical Approaches

Theoretical and empirical approaches to the design of effective messages to increase healthy and reduce risky behavior have shown only incremental progress. This article explores approaches to the development of a “recommendation system” for archives of public health messages. Recommendation systems are algorithms operating on dense data involving both individual preferences and objective message features. Their goal is to predict ratings for items (i.e., messages) not previously seen by the user on content similarity, prior preference patterns, or their combination. Standard approaches to message testing and research, while making progress, suffer from very slow accumulation of knowledge. This article seeks to leapfrog conventional models of message research, taking advantage of modeling developments in recommendation systems from the commercial arena. After sketching key components in developing recommendation algorithms, this article concludes with reflections on the implications of these approaches in both theory development and application.

The Application of Hybrid Recommendation Algorithm in Education Cloud

2014 ◽  
Vol 551 ◽  
pp. 670-674 ◽  
Author(s):  
Gai Zhen Yang
Keyword(s):  
Cloud Computing ◽  
Recommendation System ◽  
Research Work ◽  
Operating Efficiency ◽  
Cloud Platform ◽  
Recommendation Algorithm ◽  
Development Platform ◽  
Recommendation Algorithms ◽  
Technology Applications ◽  
Cloud Development

When we face large amounts of data, how can we find the most suitable educational resources quickly has become a pressing issue. In this paper, on the basic of comparative study on traditional recommendation algorithms, we use the cloud computing to solve the traditional collaborative filtering algorithms suffer from scalability issues, the proposed algorithm is applied to the combination of recommended teaching cloud platform program, the program according to different recommended by demand different recommendation strategies; open source project Hadoop as a cloud development platform of the algorithm; recommendation algorithm, algorithm on top of Hadoop to achieve improved operating efficiency is relatively high, ideal parallel performance, fully proved the cloud platform and recommended algorithm combining the advantages. The research work on the recommendation system and teaching cloud computing technology applications to provide a useful reference.

scholarly journals CMBF: Cross-Modal-Based Fusion Recommendation Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5275
Author(s):  
Xi Chen ◽  
Yangsiyi Lu ◽  
Yuehai Wang ◽  
Jianyi Yang
Keyword(s):  
Recommendation System ◽  
Fusion Method ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Actual Interaction ◽  
The Cross ◽  
Ablation Study ◽  
Available Information ◽  
The Relationship ◽  
Single Modality

A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-Modal-Based Fusion Recommendation Algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results.

scholarly journals Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sheng Bin ◽  
Gengxin Sun
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Social Relationships ◽  
Matrix Factorization ◽  
Social Relationship ◽  
Recommendation System ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

scholarly journals Research on E-Commerce Platform-Based Personalized Recommendation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Zhijun Zhang ◽  
Gongwen Xu ◽  
Pengfei Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Prediction Formula ◽  
Recommendation Algorithm ◽  
Real Dataset ◽  
Recommendation Algorithms ◽  
Filling Method ◽  
System Time ◽  
Different Levels

Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.

NCGAN:A neural adversarial collaborative filtering for recommender system

2021 ◽  
pp. 1-9
Author(s):  
Jinyang Sun ◽  
Baisong Liu ◽  
Hao Ren ◽  
Weiming Huang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Measurement Tool ◽  
Recommendation Algorithm ◽  
Generative Adversarial Network ◽  
Experimental Performance ◽  
Adversarial Network ◽  
Recommendation Algorithms ◽  
The Neural Networks ◽  
Model Training

The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users’ non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users’ ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators.

scholarly journals TRUST IN THE MUSIC? AUTOMATED MUSIC DISCOVERY, MUSIC RECOMMENDATION SYSTEMS & ALGORITHMIC CULTURE.

2019 ◽  
Vol 2019 ◽  
Author(s):  
Freeman Sophie Olivia
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Digital Culture ◽  
Music Recommendation ◽  
Private Lives ◽  
Complex Element ◽  
Recommendation Algorithms ◽  
Music Consumption ◽  
Music Streaming ◽  
Music Recommendation System

In this paper I argue that music recommendation algorithms are a complex element of contemporary digital culture. We trust music streaming and recommender systems like Spotify to ‘set the mood’ for us, to soundtrack our private lives and activities, to recommend & discover for us. These systems purport to ‘know’ us (alongside the millions of other users), and as such we let them into our most intimate listening spaces and moments. We fetishise and share the datafication of our listening habits, reflected to us annually in Spotify’s “Your 2018 Wrapped” and every Monday in ‘Discover Weekly’, even daily in the “playlists made for you”. As the accuracy of these recommendations increases, so too does our trust in these systems. ‘Bad’ or inaccurate recommendations feel like a betrayal, giving us the sense that the algorithms don’t really know us at all. Users speak of ‘their’ algorithm, as if it belonged to them and not a part of a complex machine learning recommendation system. This paper builds on research which critically examined the music recommendation system that powers Spotify and its many discovery features. The research explored the process through which Spotify automates discovery by incorporating established methods of music consumption, and demonstrated that music recommendation systems such as Spotify are emblematic of the politics of algorithmic culture.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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