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Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Aleksandra Klašnja-Milićević, Mirjana Ivanović, Boban Vesin, Zoran Budimac
2017 Applied intelligence (Boston)  
Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources.  ...  In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments.  ...  The task of tag-based personalized recommendation is to offer a learner a personalized ranked tags list for a specific item.  ... 
doi:10.1007/s10489-017-1051-8 fatcat:eqkezvtaivddxljyhmlcqgp5qy

Location-aware music recommendation using auto-tagging and hybrid matching

Marius Kaminskas, Francesco Ricci, Markus Schedl
2013 Proceedings of the 7th ACM conference on Recommender systems - RecSys '13  
We show that our approach can be scaled up using a novel music auto-tagging technique and we compare it in a live user study to: two non-hybrid solutions, either based on tags or on semantic relations;  ...  We propose a novel approach to context-aware music recommendation -recommending music suited for places of interest (POIs).  ...  We designed an evaluation study to compare the performance of the two techniques and evaluate their combination.  ... 
doi:10.1145/2507157.2507180 dblp:conf/recsys/KaminskasRS13 fatcat:jrou7wan2bf4vfozaqhoj44v5a

User-Aware Folk Popularity Rank: User-Popularity-Based Tag Recommendation That Can Enhance Social Popularity [article]

Xueting Wang, Yiwei Zhang, Toshihiko Yamasaki
2019 arXiv   pre-print
In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user  ...  A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity.  ...  ACKNOWLEDGMENTS This is a joint project between CyberBuzz, Inc. and Yamasaki lab of The University of Tokyo.  ... 
arXiv:1910.09307v1 fatcat:4xocu3qi7nfrbmgc7ro3lhwzty

A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

Noor Ifada, University of Trunojoyo Madura, Richi Nayak, Queensland University of Technology
2021 International Journal of Intelligent Engineering and Systems  
We introduce a technique to represent the users' tag preferences for leveraging the weighted-learning approach.  ...  To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank).  ...  Author Contributions The details of authors contributions are: "Conceptualization, Noor Ifada; methodology, Noor Ifada; software, Noor Ifada; validation, Noor Ifada and Richi Nayak; formal analysis, Noor  ... 
doi:10.22266/ijies2021.0228.36 fatcat:vfoqdqj4pfcmhm3glp4zrzuthm

Personalized Context-Aware Point of Interest Recommendation [article]

Mohammad Aliannejadi, Fabio Crestani
2018 arXiv   pre-print
Then, the computed scores are integrated using learning to rank techniques. The experiments on two TREC datasets show the effectiveness of our approach, beating state-of-the-art methods.  ...  We investigate four approaches to use our proposed mapping for addressing the data sparsity problem: one model to reduce the dimensionality of location taste keywords and three models to predict user tags  ...  Impact of Different Learning to Rank Techniques.  ... 
arXiv:1806.05736v1 fatcat:lblylvjqpbh2vlppf55s5alssi

Tag recommendation in software information sites

Xin Xia, David Lo, Xinyu Wang, Bo Zhou
2013 2013 10th Working Conference on Mining Software Repositories (MSR)  
TagCombine has 3 different components: 1. multilabel ranking component which considers tag recommendation as a multi-label learning problem; 2. similarity based ranking component which recommends tags  ...  Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another.  ...  We would also like to thank Wang et al. for sharing their Freecode dataset [13] .  ... 
doi:10.1109/msr.2013.6624040 dblp:conf/msr/XiaLWZ13 fatcat:6fvmozpv6nginoiuwjdt27kchu

Social tag relevance learning via ranking-oriented neighbor voting

Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian
2016 Multimedia tools and applications  
In this paper, we investigate the problem from a new perspective of learning to rank, and develop a novel approach to facilitate tag relevance learning to directly optimize the ranking performance of tag-based  ...  To improve the descriptive powers of social tags, a fundamental issue is tag relevance learning, which concerns how to interpret the relevance of a tag with respect to the contents of an image effectively  ...  From above, we can conclude that the proposed method is a highly effective technique for automatic tag recommendation.  ... 
doi:10.1007/s11042-016-3512-1 fatcat:oxwe4wikgff2tcpccvg3vchi5u

Securing Tag-based recommender systems against profile injection attacks: A comparative study [article]

Georgios Pitsilis, Heri Ramampiaro, Helge Langseth
2018 arXiv   pre-print
This work addresses challenges related to attacks on social tagging systems, which often comes in a form of malicious annotations or profile injection attacks.  ...  In particular, we study various countermeasures against two types of threats for such systems, the Overload and the Piggyback attacks.  ...  Our contribution in this paper is two fold: i) a synthetic set of malicious data to serve testing purposes, and ii) a comparative study of the effects of known attacks and the effectiveness of potential  ... 
arXiv:1808.10550v1 fatcat:kzaugb2bprfkfllnzo3ejmmeky

Tag Recommendation for Online Q A Communities based on BERT Pre-Training Technique [article]

Navid Khezrian, Jafar Habibi, Issa Annamoradnejad
2020 arXiv   pre-print
In this study, we used the BERT pre-training technique in tag recommendation task for online Q&A and open-source communities for the first time.  ...  Online Q&A and open source communities use tags and keywords to index, categorize, and search for specific content.  ...  In this study, our goal was to present a new deep learning model based on the BERT pre-training technique, which was used for the first time to assist tag recommendation in online social networking Q &  ... 
arXiv:2010.04971v1 fatcat:2txln7oj4bgzheas7mcm4a7jaa

Learning Distributed Representations for Recommender Systems with a Network Embedding Approach [chapter]

Wayne Xin Zhao, Jin Huang, Ji-Rong Wen
2016 Lecture Notes in Computer Science  
In this paper, we present a novel perspective to address recommendation tasks by utilizing the network representation learning techniques.  ...  To the best of our knowledge, it is the first time that a network representation learning approach has been applied to recommendation tasks.  ...  The authors thank the anonymous reviewers for their valuable and constructive comments.  ... 
doi:10.1007/978-3-319-48051-0_17 fatcat:x47ybfb6jzfdzg7z7ltmfasw4q

Towards a recommender strategy for personal learning environments

Felix Mödritscher
2010 Procedia Computer Science  
and iterative techniques for generating recommendations for PLEs.  ...  Due to the varying technical skills and competences of PLE users, recommendations appear to be useful for empowering learners to set up their environments so that they can connect to learner networks and  ...  Acknowledgements The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 231396 (ROLE project).  ... 
doi:10.1016/j.procs.2010.08.002 fatcat:7yjgwmuoojdfjae7pbnz5h4iqe

Living analytics methods for the social web

Ernesto Diaz-Aviles
2013 SIGIR Forum  
We believe that answering many of those questions will allow us to build more intelligent systems for the benefit of society.  ...  However, research on Living Analytics as unified field is very new, and there are many broad open questions to consider as outlined in the Introduction.  ...  Although our investigation has provided promising results, we believe that our contribution is an initial step in the study of SI techniques for Learning to Rank and Recommender Systems, additional research  ... 
doi:10.1145/2568388.2568412 fatcat:bvpurklgi5a3rgvb4ve2tk2ohq

Tag-based collaborative filtering recommendation in personal learning environments

Mohamed Amine Chatti, Simona Dakova, Hendrik Thus, Ulrik Schroeder
2013 IEEE Transactions on Learning Technologies  
The personal learning environment (PLE) concept offers a learner-centric view of learning and suggests a shift from knowledge-push to knowledge-pull approach to learning.  ...  In this paper, we study different tag-based collaborative filtering recommendation techniques on their applicability and effectiveness in PLE settings.  ...  The aim of the study is to experiment with different tag-based CF recommendation algorithms, memory based as well as model based, in a PLE context.  ... 
doi:10.1109/tlt.2013.23 fatcat:qi7qduglgrfr5aagfiqxhym2fm

How Relevant is the Irrelevant Data

Noor Ifada, Richi Nayak
2016 Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16  
To infer the latent factors of tensor models correctly to produce the high quality recommendation, we develop a novel learning-torank method, Go-Rank, that optimizes Graded Average Precision (GAP).  ...  For the task of tag-based item recommendations, the underlying tensor model faces several challenges such as high data sparsity and inferring latent factors effectively.  ...  This is the first time GAP has been proposed to optimize a learning-to-rank model for recommendation method using tagging data as implicit feedback.  ... 
doi:10.1145/2835776.2835790 dblp:conf/wsdm/IfadaN16 fatcat:vegnwhmilnggpl5fifhb4g6h4e

Article Recommendation and Comics Story Representation for Twitter User Based Preferences

S. Vivekanandan, Swathi. N
2020 International Journal of Scientific Research in Science and Technology  
We filter the stream of incoming tweets to remove junk tweets using a text classification algorithm.We also compare the performance of different supervised SVM text classification algorithms for this task  ...  Our method provides an efficient way to accurately categorize comic topic recommendation without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly  ...  RankSVM algorithm is used to rank the different tags of the same user and Rank Aggregation algorithm is used for ranking the overall tags hence a customized recommendation of news articles is provided  ... 
doi:10.32628/ijsrst207226 fatcat:wmxrxaxmxrbc7pi5jdq4h4g6ri
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