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Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback

Markus Zanker, Markus Jessenitschnig
2009 2009 IEEE Conference on Commerce and Enterprise Computing  
Keywords-Collaborative filtering, hybrid recommendation methods, cold-start problem Zanker M., Jessenitschnig M.: Collaborative feature-combination recommender exploiting explicit and implicit user feedback  ...  Collaborative filtering (CF) is currently the most popular technique used in commercial recommender systems.  ...  2) How does a feature-combination hybrid improve results in terms of accuracy and user coverage compared to standard collaborative filtering?  ... 
doi:10.1109/cec.2009.84 dblp:conf/wecwis/ZankerJ09 fatcat:ex7udmlwwzegfab6vpkbqa4cr4

A Movie Recommendation using Common Genre Relation on User-Item Subgroup

2019 International Journal of Engineering and Advanced Technology  
Meanwhile, the method reduces the search space for each user and helps to mitigate the sparsity problem. To improve the scalability, the methods are executed on user-item subgroups.  ...  The empirical analysis shows that the proposed method based on the graph model excels the accuracy at top-k than the state-of-art collaborative filtering methods.  ...  User-Item Subgroups and Collaborative Filtering Clustering is an unsupervised learning technique commonly used in CF based recommendations to improve the scalability.  ... 
doi:10.35940/ijeat.a1038.1291s319 fatcat:iub4g4xtobehbccmqrb4zc7xnq

Enhancement of collaborative filtering using myers-briggs type indicator (mbti) applied in recommendation system

Arvin Jay C Nadala
2022 South asian journal of engineering and technology  
Collaborative filtering is one of the most popular recommender systems being used today.  ...  Related items are expected to be recommended according to his neighbor's similar interests or inclinations.  ...  Since collaborative filtering utilizes ratings from similar users, there will be no items that can be recommended to User 3 which causes a cold-start problem.  ... 
doi:10.26524/sajet.2022.12.16 fatcat:eyyxasjaqzh4jngqd27ppp344i

From "I Like" to "I Prefer" in Collaborative Filtering

Armelle Brun, Ahmad Hamad, Olivier Buffet, Anne Boyer
2010 2010 22nd IEEE International Conference on Tools with Artificial Intelligence  
Collaborative filtering exploits user preferences, generally ratings, to provide them with recommendations.  ...  First experiments show that this new approach compares with, and sometimes improves, the classical one.  ...  RSs generally fall into three categories, based on the information they use to perform recommendations [1] : content-based systems, knowledge-based systems and collaborative-filtering (CF) systems.  ... 
doi:10.1109/ictai.2010.129 dblp:conf/ictai/BrunHBB10 fatcat:cf4xiuvrxvhybevg73acigomre

Recommender Systems Meeting Security: From Product Recommendation to Cyber-Attack Prediction [chapter]

Nikolaos Polatidis, Elias Pimenidis, Michalis Pavlidis, Haralambos Mouratidis
2017 Communications in Computer and Information Science  
The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network.  ...  Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation.  ...  This method applies collaborative filtering and then rearranges the order of the k nearest neighbors according to the similarity value and the number of co-rated items.  ... 
doi:10.1007/978-3-319-65172-9_43 fatcat:onz52y3h5vel3pb64zoyzuckpm


2014 Journal of Computer Science  
users or items, which leads to degraded recommendation quality.  ...  Collaborative filtering is one of the popular approaches for providing recommendation.  ...  This study focuses on improving the recommendation quality in collaborative filtering recommender system, by tackling the cold start problem.  ... 
doi:10.3844/jcssp.2014.1166.1173 fatcat:qsdv5pexvbdapd2kxpcjfju2gy

Case-studies on exploiting explicit customer requirements in recommender systems

Markus Zanker, Markus Jessenitschnig
2008 User modeling and user-adapted interaction  
Recommender Systems (RS) suggest useful and interesting items to users in order to increase user satisfaction and online conversion rates.  ...  Its contribution lies in comparing different techniques such as knowledge-and utility-based methods, collaborative filtering, association rule mining as well as hybrid variants when user models consist  ...  Acknowledgements The authors would like to thank Arthur Pitman, Birgit Winkler and Marion Kollmann for proofreading and anonymous reviewers for their valuable and helpful comments.  ... 
doi:10.1007/s11257-008-9048-y fatcat:52ppiafdorcnthyq5sbg3yvlzm

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  ...  Recently, based on the intuition that additional information provides useful insights for matrix factorization techniques, several recommendation algorithms have utilized additional information to improve  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous referees and the editor for their helpful comments and suggestions.  ... 
arXiv:1405.0770v1 fatcat:oqz3oxxlffctfg5ybflyif2hmy

A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations

Vijay Verma, Rajesh Kumar Aggarwal
2019 International Journal of Information Technology and Computer Science  
Neighborhood-based approaches are traditional techniques for collaborative recommendations and are very popular due to their simplicity and efficiency.  ...  Recommender Systems (RSs) are essential tools of an e-commerce portal in making intelligent decisions for an individual to obtain product recommendations.  ...  CF4J library has been designed to carry out collaborative filtering-based recommendation research experiments.  ... 
doi:10.5815/ijitcs.2019.06.05 fatcat:px3mnqs3irh4hbrww4c7tz2hni

Personality-Aware Collaborative Filtering: An Empirical Study in Multiple Domains with Facebook Data [chapter]

Ignacio Fernández-Tobías, Iván Cantador
2014 Lecture Notes in Business Information Processing  
In this paper we investigate the incorporation of information about the users' personality into a number of collaborative filtering methods, aiming to address situations of user preference scarcity.  ...  Through empirical experiments on a multi-domain dataset obtained from Facebook, we show that the proposed personality-aware collaborative filtering methods effectively -and consistently in the studied  ...  In fact, several authors [9, 11, 17, 18] have already explored how user personality can be exploited to improve collaborative filtering recommendations.  ... 
doi:10.1007/978-3-319-10491-1_13 fatcat:awqkahtwpzetdapdrxr3jzipyu

Mining Implicit Correlations between Users with the Same Role for Trust-Aware Recommendation

2015 KSII Transactions on Internet and Information Systems  
In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation.  ...  Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation.  ...  CF-TCMe and CF-TC-MF exploit the memory-based collaborative filtering and matrix-factorization-based collaborative filtering to predict ratings.  ... 
doi:10.3837/tiis.2015.12.009 fatcat:sbbpcoomxrdqjbf3brk5ibz7qq

Research on Improved Collaborative Filtering Recommendation Algorithm on Hadoop

Baojun Tian, Xiaojuan Du, Peipei Hu, Yila Su
2016 International Journal of Control and Automation  
To address the cold-start issue, we propose a hybrid recommendation method that combines collaborative filtering and content-based filtering exploiting the advantages of both methods.  ...  In this paper, we propose an improved collaborative filtering algorithm that aims to address these issues.  ...  Memory-based collaborative filtering algorithm is divided into user-based and item-based. It utilizes the entire user-item dataset to make predictions.  ... 
doi:10.14257/ijca.2016.9.12.33 fatcat:innyq2limjgjlh6nrzvkoqyipi

Personalized Recommender by Exploiting Domain based Expert for Enhancing Collaborative Filtering Algorithm :PReC

Mrs.M Sridevi, Dr.R.Rajeswara Rao
2019 International Journal of Advanced Computer Science and Applications  
Personalized Expert based collaborative filtering (PReC) approach is proposed to identify domain specific experts and the use of experts preference enhanced the performance of collaborative filtering recommender  ...  Collaborative filtering is one of the most traditional and intensively used recommendation approaches for many commercial services.  ...  to improve the performance of traditional collaborative filtering recommender systems.  ... 
doi:10.14569/ijacsa.2019.0100313 fatcat:j54k25smrrgwjo56agrtefwodm

Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems

Ayman S. Ghabayen, Shahrul Azman Mohd Noah
2017 International Journal on Advanced Science, Engineering and Information Technology  
Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations.  ...  Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to "cold-start" users, for example, where often there are insufficient preferences to make recommendations  ...  We believe that semantic tags can tackle the limitations inherent in traditional collaborative filtering and improve the quality of collaborative filtering by capturing users' semantic preferences based  ... 
doi:10.18517/ijaseit.7.6.1826 fatcat:dcbfr6r7nrdtbfujrnmdwb7kpu

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

Huirui Han, Mengxing Huang, Yu Zhang, Uzair Aslam Bhatti
2018 Information  
In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance  ...  Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization.  ...  Acknowledgments: The authors would like to thank the editor and anonymous referees for the constructive comments in improving the contents and presentation of this paper.  ... 
doi:10.3390/info9060143 fatcat:4pghffli5vampb36iexa36x2te
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