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Refining User and Item Profiles based on Multidimensional Data for Top-N Item Recommendation
2014
Proceedings of the 16th International Conference on Information Integration and Web-based Applications & Services - iiWAS '14
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approaches for top-N item recommendations based on the proposed user/item profiles. ...
In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. ...
The top-N item recommendation task for multidimensional data has been tackled in many different ways. ...
doi:10.1145/2684200.2684284
dblp:conf/iiwas/TangXG14
fatcat:od7nqrm5xjbw5c2wkdwkxelp24
Incorporating contextual information in recommender systems using a multidimensional approach
2005
ACM Transactions on Information Systems
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A multidimensional approach to recommender systems is presented that provides recommendations based on various contextual information in addition to the typical information on users and items used in most ...
To test the proposed approach in practice, a multidimensional application for recommending movies was developed and the combined approach implemented. ...
Instead of providing the standard recommendation of "top N items to a user," we can now use the available profiling information to provide more targeted recommendations, such as recommending "top 3 action ...
doi:10.1145/1055709.1055714
fatcat:j67rpuusenalrlcmny2qv5e374
Movie Recommendation System
2022
International Journal for Research in Applied Science and Engineering Technology
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The presented recommender system generates recommendations using various types of knowledge and data about users, the available items, and previous transactions stored in customized databases. ...
It is based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time. ...
Collaborative Filtering Collaborative filtering system recommends items based on similarity measures between users and/or items. ...
doi:10.22214/ijraset.2022.40225
fatcat:beilg3fokfctplixlu4hq34p5i
Collaborative filtering in social tagging systems based on joint item-tag recommendations
2010
Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10
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These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. ...
Based on this data model, we further propose a unified user profiling scheme to make full use of all available information. ...
parameters -, γ, , and , and the number of items/tags to be recommended -N 1.Construct a profile matrix for each user based on the training data following equations (1a) ~ (1d) ...
doi:10.1145/1871437.1871541
dblp:conf/cikm/PengZZW10
fatcat:aw2txylqxvb6hln2fofqdj2rdy
$$\mathcal {IRORS}$$ IRORS : intelligent recommendation of RSS feeds
2016
Vietnam Journal of Computer Science
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However, the user is flooded by the amount of such RSS feeds. For that reason, any analysis of RSS feeds seems effortful and complex. ...
The abundance of information prohibits getting relevant results on online social researches. Thus, RSS feeds appear as monitoring tool of current events according to users preferences. ...
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s40595-015-0054-9
fatcat:kgebmo22zvbptp5ksj2uwxcl5i
A Movie Recommender System: MOVREC
2015
International Journal of Computer Applications
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The presented recommender system generates recommendations using various types of knowledge and data about users, the available items, and previous transactions stored in customized databases. ...
It is based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time. ...
Content-based filtering approach
Collaborative filtering Collaborative filtering system recommends items based on similarity measures between users and/or items. ...
doi:10.5120/ijca2015904111
fatcat:zys3sllv6vco7aaydc4n6m5dwm
Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities
2021
IEEE Access
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These systems are able to detect specific user needs and adapt recommendations to actual user context. ...
Her research interests include recommender system, information retrieval, social network, data mining and machine learning. ...
ACKNOWLEDGEMENTS This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University. The authors are grateful for this support. ...
doi:10.1109/access.2021.3072165
fatcat:i3igbxd44jhrzcyvynevpidcwq
Recommender Systems Based on Evolutionary Computing: A Survey
2017
Journal of Software Engineering and Applications
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The recommender systems try to recommend the most suitable items to the target users by investigating a user's interest in an item and the interactions between users and users or users and items. ...
One of the several techniques having been investigated for the development of RS in the How to cite this paper: Sadeghi, M. and Asghari, S.A. (2017) Recommender Systems Based on Evolutionary Computing: ...
The paper [59] proposed a framework and used the multi-objective evolutionary algorithm based on decomposition to improve the novelty and the diversity for top-k recommendations of an item-based collaborative ...
doi:10.4236/jsea.2017.105023
fatcat:qsmw7jgja5g4fe7mfsf2rynyta
A Pre-filtering Approach for Incorporating Contextual Information into Deep Learning Based Recommender Systems
2020
IEEE Access
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Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information. ...
However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations. ...
The rationale for selecting this metric is to evaluate the Top-N recommendations and in this case quantifying the times that the top element in the Top-N list matches the target. ...
doi:10.1109/access.2020.2975167
fatcat:ynobpk4bv5fcln7zocyp546hze
Multidimensional Group Recommendations in the Health Domain
2020
Algorithms
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However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. ...
Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients' welfare. ...
Algorithm 1: Fair Group Recommendations Algorithm Data: A group of users G = {u 1 , . . . , u n }, the sets of recommendations A u x , ∀u x ∈ G. ...
doi:10.3390/a13030054
fatcat:kzugfkovxjcxtd737en3cevo5q
Personalized recommendation on dynamic content using predictive bilinear models
2009
Proceedings of the 18th international conference on World wide web - WWW '09
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Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. ...
In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. ...
ACKNOWLEDGMENTS We thank Raghu Ramakrishnan, Scott Roy, Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, and Ajoy Sojan for many discussions and helps on data collection. ...
doi:10.1145/1526709.1526802
dblp:conf/www/ChuP09
fatcat:jf3lp5e46zhynpzgppvng3gfam
Semantics-Aware Autoencoder
2019
IEEE Access
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Accuracy in the recommendation is no more enough since users are also expecting a useful explanation for the suggested items. Users, on the other hand, want to know why. ...
They help users to find what they are looking for by suggesting relevant items leveraging their past preferences. ...
We use then ratings from Equation ( 5 ) to provide top-N recommendation for each user.
B. ...
doi:10.1109/access.2019.2953308
fatcat:7twjp4f2evh25bcm4kjtyzpmna
Exploring Contextual Paradigms in Context-Aware Recommendations
2020
2020 IEEE International Conference on Big Data (Big Data)
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Traditional recommendation systems utilise past users' preferences to predict unknown ratings and recommend unseen items. ...
In this work, we explore these three ways of incorporating context in the recommendation pipeline, and compare them on context-aware datasets with different characteristics. ...
Each user will have a predicted top-k list and an actual top-k list based off their actual ratings. ...
doi:10.1109/bigdata50022.2020.9377964
fatcat:cdeqw4sh5rdztkxmhzx7oglapq
Context-Aware Recommender Systems
[chapter]
2010
Recommender Systems Handbook
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While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into ...
, data mining, marketing, and management. ...
The authors thank YoungOk Kwon for editorial assistance. ...
doi:10.1007/978-0-387-85820-3_7
fatcat:vpdyxzgw6rca7hmyznasnzh6c4
Context-Aware Recommender Systems
[chapter]
2015
Recommender Systems Handbook
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While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into ...
, data mining, marketing, and management. ...
The authors thank YoungOk Kwon for editorial assistance. ...
doi:10.1007/978-1-4899-7637-6_6
fatcat:qsgwjdk2tndljfovxmeu7rpu6i
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