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Chen Lin, Runquan Xie, Lei Li, Zhenhua Huang, Tao Li
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques.  ...  A variety of news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers.  ...  For nop, MF randomizes a user factor, and generates ratings from the pseudo user factor and the item factor; LDA-filtering randomizes the topic distribution for the new user, and generates ratings by the  ... 
doi:10.1145/2396761.2398482 dblp:conf/cikm/LinXLHL12 fatcat:lf5fuyax7natljflbq2ydaer6m

Recommendation with Multi-Source Heterogeneous Information

Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source  ...  Specifically, we combine item structure, textual content and tag information for recommendation.  ...  Acknowledgments We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Key  ... 
doi:10.24963/ijcai.2018/469 dblp:conf/ijcai/GaoYWZLH18 fatcat:63fvrois7ffsfk2ojwp3yeymly

Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works

Ayush Singhal, Pradeep Sinha, Rakesh Pant
2017 International Journal of Computer Applications  
We organize the review in three parts: Collaborative system, Content based system and Hybrid system.  ...  With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most  ...  The model also uses a shared layer to couple the item features with user behaviors.  ... 
doi:10.5120/ijca2017916055 fatcat:m6icpquumbgczhrdnya7x35of4

Time aware topic based recommender system

Emad Gohari Boroujerdi, Heidar Davoudi, Aijun An, Elnaz Delpisheh
2016 Big Data & Information Analytics  
for new users or items.  ...  In this approach, the introduction of new users or new items can cause the cold start problem, as there will be insufficient data on these new entries for the collaborative filtering to draw any inferences  ...  We would like to thank the data science group at The Globe and Mail, in particular, Michael O'Neill, Gordon Edall, and Shengqing Wu, for providing us with the data set used in this research, and insight  ... 
doi:10.3934/bdia.2016008 fatcat:dm42afm7yfdkbiz6zivds5t6nq

A Multi-Modality Deep Network for Cold-Start Recommendation

2018 Big Data and Cognitive Computing  
Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions.  ...  However, CF approaches suffer from the cold-start problem for users and items with few ratings.  ...  Acknowledgments: We would like to thank the reviewers for their constructive and insightful comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/bdcc2010007 fatcat:b22oovdzyjaetlxjvpepj2bbpq


Ssvr Kumar Addagarla
2019 International Journal of Electronic Commerce Studies  
This paper investigates the various traditional Recommendation System like Content-based (CB), Collaboration Filtering-based (CF), Demographic-based, Knowledge-based and discussed current trends in recommendation  ...  This paper wellelaborated for the past, present and future scope of the Recommendation System which would be useful for researchers to get familiarity with this domain.  ...  Knowledge-based RS In collaboration filtering, RS users are correlated with the item ratings.  ... 
doi:10.7903/ijecs.1705 fatcat:puqvc6uhd5dhppuatartqqq6ki

Personalized news recommendation via implicit social experts

Chen Lin, Runquan Xie, Xinjun Guan, Lei Li, Tao Li
2014 Information Sciences  
of each user, together with the content of each news story.  ...  Collaborative filtering and the recent trend of social networking approaches (i.e. explore the potential of "word of mouth" in social trust network) [29, 17] are generally not applicable to new users and  ...  A topic category is learned for each article to predicted user interest content-wise, such a score is then multiplied with the collaborative score to generate final news recommendations [27] .  ... 
doi:10.1016/j.ins.2013.08.034 fatcat:lrx7gvofavhotiohm2xfr4rw2i

The Continuous Cold Start Problem in e-Commerce Recommender Systems [article]

Lucas Bernardi and Jaap Kamps and Julia Kiseleva and Melanie JI Müller
2015 arXiv   pre-print
This paper exposes the 'Continuous Cold Start' (CoCoS) problem and its consequences for content- and context-based recommendation from the viewpoint of typical e-commerce applications, illustrated with  ...  However, many real-life e-commerce applications suffer from an aggravated, recurring version of cold-start even for known users or items, since many users visit the website rarely, change their interests  ...  Several approaches have been proposed and successfully applied to deal with the cold-start problem, such as utilizing baselines for cold users [8] , combining collaborative filtering with content-based  ... 
arXiv:1508.01177v1 fatcat:bt3ssvv7jrby3bxfn5dj5xoumu

Towards Intelligent and Adaptive Digital Library Services [chapter]

Md Maruf Hasan, Ekawit Nantajeewarawat
2008 Lecture Notes in Computer Science  
Collaborative filtering in general works as follows (see Figure 2 .8).  ...  TechLens developed a generic DL recommendation model using the Collaborative filtering approach by analyzing relationships between citations.  ... 
doi:10.1007/978-3-540-89533-6_11 fatcat:4vielaabqnfalebbtc7p3pl5ky

The evolution of travel recommender systems: A comprehensive review

Muneer V. K., K. P. Mohamed Basheer
2020 Malaya Journal of Matematik  
Personalized Travel RS will add more customization and user-specific features than Automatic Travel RS.  ...  Planning a trip is a time-consuming and herculean task for inexperienced travelers. Here comes the possibility of expert opinion for scheduling a perfect travel plan.  ...  Al [39] , introduced Collaborative Topic Regression (CTR), which is a tightly coupled method and a probabilistic graphical model.  ... 
doi:10.26637/mjm0804/0075 fatcat:x2f34v67hfg75le7tquws3siem

Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments [article]

Alexandrin Popescul, Lyle H. Ungar, David M Pennock, Steve Lawrence
2013 arXiv   pre-print
We propose a unified probabilistic framework for merging collaborative and content-based recommendations.  ...  We extend Hofmann's [1999] aspect model to incorporate three-way co-occurrence data among users, items, and item content.  ...  Hofmann and Puzicha (1999) apply the aspect model to user-item co-occurrence data for collaborative filtering.  ... 
arXiv:1301.2303v1 fatcat:rrjeeumykfhcfbzh2y7kye3nte

Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback [chapter]

Haochao Ying, Liang Chen, Yuwen Xiong, Jian Wu
2016 Lecture Notes in Computer Science  
Collaborative Filtering with Implicit Feedbacks (e.g., browsing or clicking records), named as CF-IF, is demonstrated to be an effective way in recommender systems.  ...  To address this problem, we propose collaborative deep ranking (CDR), a hybrid pair-wise approach with implicit feedback, which leverages deep feature representation of item content into Bayesian framework  ...  In CTR, item content based on probabilistic topic model incorporates into traditional collaborative filtering.  ... 
doi:10.1007/978-3-319-31750-2_44 fatcat:hb6oty4ntrhabccfmgcju5iuoa

A Review on Personalized Academic Paper Recommendation

Zhi Li, Xiaozhu Zou
2019 Computer and Information Science  
With the advent of the era of big data, it has become extremely easy for scientific users to have to access academic papers, which has enhanced their efficiency and capacity to search or browse papers.  ...  assist academic users doing research.  ...  Acknowledgments The research is financed by Graduates Scientific Research Innovation Project of NUAA "Research on characteristic database Construction of science and technology report in helicopter topic  ... 
doi:10.5539/cis.v12n1p33 fatcat:dyks7j5egjbyfdopthm57qhta4

Hierarchical Bayesian Models for Collaborative Tagging Systems

Markus Bundschus, Shipeng Yu, Volker Tresp, Achim Rettinger, Mathaeus Dejori, Hans-Peter Kriegel
2009 2009 Ninth IEEE International Conference on Data Mining  
Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike.  ...  By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining.  ...  CONCLUSION AND OUTLOOK In this paper, we presented hierarchical Bayesian models for mining and modelling large systems with user generated content and massive annotation.  ... 
doi:10.1109/icdm.2009.121 dblp:conf/icdm/BundschusYTRDK09 fatcat:6vpldv2llfcxtjojhuvw2chciu

Deep Exercise Recommendation Model

Tuanji Gong, Xuanxia Yao
2019 International Journal of Modeling and Optimization  
We use tightly couple model to combine SDAE model and collaborative filter model.  ...  In this paper, we propose a new hybrid recommendation model that combines deep collaborative filtering (DeepCF) component with wide linear component.  ...  generative model called collaborative variant auto encoder (CVAE) that considered both rating and content for recommendation in multimedia scenario.  ... 
doi:10.7763/ijmo.2019.v9.677 fatcat:zlghod2ugjetlghcod3onoewgq
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