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A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering [article]

Xiaoxue Zhao, Jun Wang
2016 arXiv   pre-print
In this paper, we present a theoretical framework for tackling the cold-start collaborative filtering problem, where unknown targets (items or users) keep coming to the system, and there is a limited number  ...  In this paper, we study a simple two-stage recommendation combining a sequential and a batch solution together.  ...  Conclusion and Future Work In this paper, we presented a novel two-stage recommendation process to address the cold-start problems, with an item cold-start problem as a working example.  ... 
arXiv:1601.04745v1 fatcat:pswvl2noyjembokkare4kjzioq

Retracted: E-Learning Recommender Systems Based on Goal-Based Hybrid Filtering

Muhammad Waseem Chughtai, Ali Selamat, Imran Ghani, Jason J. Jung
2014 International Journal of Distributed Sensor Networks  
The proposed work consists of two hybridizations: the first hybridization has been made with content-based filtering and collaborative features to overcome the new-learners zero-rated profile recommendations  ...  Therefore, the proposed goal-based hybrid filtering approach that hybridized content-based filtering, collaborative filtering and k-neighborhood features simultaneously works on both types of learner's  ...  From a critical and a deep analysis, Table 1 shows that the cold-start problem is one of a common problems in recommender systems.  ... 
doi:10.1155/2014/912130 fatcat:7kh7hfmrffhrzpqzvbb5fpz3wq

Recommendation and Classification Systems: A Systematic Mapping Study

J. G. Enríquez, L. Morales-Trujillo, Fernando Calle-Alonso, F. J. Domínguez-Mayo, J. M. Lucas-Rodríguez
2019 Scientific Programming  
earlier stages such as business requirements and analysis.  ...  Conclusions show that the combination of these two algorithms (classification and recommendation) is not very used in practice.  ...  Acknowledgments is research has been supported by the Pololas project (TIN2016-76956-C3-2-R) of the Spanish Ministry of Economy and Competitiveness, the ADAGIO (P106-16/ E09) project of the Centro para  ... 
doi:10.1155/2019/8043905 fatcat:wx734w2af5cpheoijkanvvzdbm

Modern Approaches to Building Recommender Systems for Online Stores

Lyudmila Onokoy, Jurijs Lavendels
2019 Applied Computer Systems  
Of greatest interest are the criteria for selecting effective methods for specific online stores and the authors' concept of a typical recommender system of electronic commerce.  ...  The article presents current approaches to solving the problem of building recommender systems designed to intellectualize the user interface of online stores.  ...  methods: − Advantages: the methods are devoid of the problems of collaborative filtering, such as the sparseness of the rating matrix, the cold start, since the methods are based on the analysis of the  ... 
doi:10.2478/acss-2019-0003 fatcat:7erpfcv7qvfynbc7ea56eezjze

PRODUCT RECOMMENDATION FRAMEWORK BASED ON CUSTOMER REVIEW USING COLLABORATIVE FILTERING TECHNIQUESL

C Bharathipriya
2020 JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES  
This work gives a recommender system which increases the value of e-commerce websites and worthiness in encountering best products for customers.  ...  Using Recommender system, Business to Consumer (B2C) relationship can be benefitted and optimal, product selection is generated by solving voluminous data dynamically .In this work, a collaborative filtering  ...  Hybrid approach is helpful in cold start problems and handle data sparsity problem. Collaborative Filtering Collaborative filtering has a major role in recommender system.  ... 
doi:10.26782/jmcms.spl.7/2020.02.00004 fatcat:w6jtpuqii5enrleg5k24mirtfq

Teaching Design of "Three-Dimensional" Blended Ideological and Political Courses from the Perspective of Deep Learning

Hui Dong, Muhammad Arif
2022 Security and Communication Networks  
This paper develops a college ideological education course recommendation system based on deep learning, based on a hybrid collaborative filtering algorithm, and by introducing the effectiveness of the  ...  gradually forgetting curve based on changes in user feature, it better solves the shortcomings of traditional collaborative filtering algorithms, such as low efficiency and weak adaptability.  ...  Collaborative filtering algorithm also has inherent defects, mainly reflected in the cold start mode of initial data, which requires the collection of a large number of user behavior lists in the start-up  ... 
doi:10.1155/2022/6046243 fatcat:vu63pouc45ehxdbwd7sji4fvsu

A Monte Carlo algorithm for cold start recommendation

Yu Rong, Xiao Wen, Hong Cheng
2014 Proceedings of the 23rd international conference on World wide web - WWW '14  
Specifically we define a random walk on a bipartite graph of users and items to simulate the preference propagation among users, in order to alleviate the data sparsity problem for cold start users.  ...  Theoretical analysis is presented to demonstrate the efficiency and effectiveness of our algorithm, and extensive experiments also confirm our theoretical findings.  ...  One of the most popular recommendation frameworks to address this problem is Collaborative Filtering (CF).  ... 
doi:10.1145/2566486.2567978 dblp:conf/www/RongWC14 fatcat:bee5hrgjwjgcplyn5kjctx4xoa

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

Liang Jiang, Lu Liu, Jingjing Yao, Leilei Shi
2020 Journal of Cloud Computing: Advances, Systems and Applications  
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.  ...  Competing interests The authors declare no conflict of interest. Author details Received: 15 January 2020 Accepted: 8 September 2020  ... 
doi:10.1186/s13677-020-00199-2 fatcat:wn5xlhmz5nczdgmstwye3xrh7q

Social and Content Hybrid Image Recommender System for Mobile Social Networks

Faustino Sanchez, Marta Barrilero, Silvia Uribe, Federico Alvarez, Agustin Tena, Jose Manuel Menendez
2012 Journal on spesial topics in mobile networks and applications  
In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run  ...  The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based  ...  Moreover, the authors thank Alvaro Martínez and lago Fernández-Cedrón for the help with the implementation of the Android application, and Javier Arróspide for the English language review.  ... 
doi:10.1007/s11036-012-0399-6 fatcat:wcdwfyfqdfashbbybgb6wbtjw4

Corpus-Driven Resource Recommendation Algorithm for English Online Autonomous Learning

Ling Gu, Naeem Jan
2022 Computational and Mathematical Methods in Medicine  
Furthermore, schools and families do not prioritize English learning, resulting in low teacher expectations for English instruction, as well as a lack of English practice and strategy training among students  ...  The model is optimized in the aspects of high efficiency, diversity, and timeliness of learning resource recommendation supported by deep learning technology.  ...  One of the most significant issues in collaborative filtering recommendation systems is cold start.  ... 
doi:10.1155/2022/9369258 pmid:35747131 pmcid:PMC9211380 fatcat:afcoc7itwbfrfagi36n23pmya4

Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Shameem A. Puthiya Parambath, Sanjay Chawla
2020 Data mining and knowledge discovery  
We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items.  ...  Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem.  ...  To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.  ... 
doi:10.1007/s10618-020-00708-6 fatcat:xhor425vmfe5bljgggn5bexpxi

Research on Precision Teaching Model of Ideology Course Based on Collaborative Filtering Algorithm

Jinshan Li, Chin-Ling Chen
2022 Security and Communication Networks  
For the data sparseness and cold start of collaborative filtering algorithm, the course feature attributes and attribute value preference matrix are used to solve the problem, and the similarity is calculated  ...  In order to verify the effectiveness of this method for precise teaching, we conducted a test.  ...  Acknowledgments is research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  ... 
doi:10.1155/2022/5272779 fatcat:43wk6pzvpbc2xj4sbels3z7fj4

Recommender systems survey

J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez
2013 Knowledge-Based Systems  
This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems  ...  Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering.  ...  At this stage, hybrid approaches (primarily collaborative-demographic and collaborative-content filtering) improved the quality of the recommendations.  ... 
doi:10.1016/j.knosys.2013.03.012 fatcat:z3gc5qjhkrcd5dsaah2gjdyu3y

Analyzing weighting schemes in collaborative filtering

Alan Said, Brijnesh J. Jain, Sahin Albayrak
2012 Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12  
Collaborative filtering recommender systems provide their users with relevant items based on information from other similar users.  ...  In this paper, we investigate the effects of common weighting schemes on different types of users, i.e. new users with few ratings (so-called cold start users), post cold start users, and power users.  ...  The work in this paper was conducted in the scope of the KMulE project which was sponsored by the German Federal Ministry of Economics and Technology (BMWi).  ... 
doi:10.1145/2245276.2232114 dblp:conf/sac/SaidJA12 fatcat:djmwhdr2mnge3paci3wkvium2y

Attributes Coupling based Item Enhanced Matrix Factorization Technique for Recommender Systems [article]

Yonghong Yu, Can Wang, Yang Gao
2014 arXiv   pre-print
Matrix factorization technique is one of the most widely employed collaborative filtering techniques in the research of recommender systems due to its effectiveness and efficiency in dealing with very  ...  Experimental results on two real data sets show that our proposed method outperforms state-of-the-art recommendation algorithms and can effectively cope with the cold start item problem when more item  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous referees and the editor for their helpful comments and suggestions.  ... 
arXiv:1405.0770v1 fatcat:oqz3oxxlffctfg5ybflyif2hmy
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