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Content-based Music Recommendation: Evolution, State of the Art, and Challenges [article]

Yashar Deldjoo, Markus Schedl, Peter Knees
2021 arXiv   pre-print
Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies.  ...  In the past years, music recommendation models that leverage collaborative and content data -- which we refer to as content-driven models -- have been replacing pure CF or CB models.  ...  To wrap up, we hope and believe that the presented survey will serve as a standard for categorizing music content, as a reference to the evolution and state of the art in content-driven music recommendation  ... 
arXiv:2107.11803v1 fatcat:4hz4hqkkmvcapbdr3wvtp2t4iu

Towards Cognitive Recommender Systems

Amin Beheshti, Shahpar Yakhchi, Salman Mousaeirad, Seyed Mohssen Ghafari, Srinivasa Reddy Goluguri, Mohammad Amin Edrisi
2020 Algorithms  
We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven  ...  Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a13080176 fatcat:m6fijkw7srecdhqylayx5u2aay

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of  ...  The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.  ...  An overview of state-of-the-art AI in recommender systems including models, methods and applications; 3.  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy

Explainability in Music Recommender Systems [article]

Darius Afchar, Alessandro B. Melchiorre, Markus Schedl, Romain Hennequin, Elena V. Epure, Manuel Moussallam
2022 arXiv   pre-print
be adapted -- to the specific characteristics of music consumption and recommendation.  ...  Finally, we describe the current challenges for introducing explainability within a large-scale industrial music recommender system and provide research perspectives.  ...  ACKNOWLEDGMENTS This work received support from the Austrian Science Fund (FWF): P33526 and DFH-23.  ... 
arXiv:2201.10528v1 fatcat:k4mhwh2nhzexxgyvz3dppv3j5i

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Priyanka Rastogi, Dr. Vijendra
2016 International Journal of Advanced Computer Science and Applications  
But the key challenge lies in what all information can be collected and assimilated to make effective recommendations.  ...  This paper discusses reasons for evolution of recommender systems leading to transition from traditional to social information based recommendations.  ...  of social contextual information and the user-item rating matrix, based on a probabilistic matrix factorization Epinions , MovieLens Movies Recommendation Outperformed the other state-of-the-art  ... 
doi:10.14569/ijacsa.2016.070420 fatcat:wwvyumdr5ffohiphirbb3ycuoa

Shopping Center Tracking and Recommendation Systems [chapter]

Ricardo Anacleto, Nuno Luz, Ana Almeida, Lino Figueiredo, Paulo Novais
2011 Advances in Intelligent and Soft Computing  
From this overview, a high-level model of the domain (involving actors and services) is described along with challenges and possible features in the context of current Semantic Web, mobile device and sensor  ...  However, due to the environment complexity, several feasibility issues emerge when designing both the logical and physical architecture of such systems.  ...  In this paper, we present the current state of the art in shopping center tracking and recommendation systems.  ... 
doi:10.1007/978-3-642-19644-7_32 dblp:conf/softcomp/AnacletoLAFN11 fatcat:ocriqrqahvattcqba7un6ytq7u

News Recommender System: A review of recent progress, challenges, and opportunities [article]

Shaina Raza, Chen Ding
2021 arXiv   pre-print
In this paper, we highlight the major challenges faced by the news recommendation domain and identify the possible solutions from the state-of-the-art.  ...  By providing the state-of-the-art knowledge, this survey can help researchers and practical professionals in their understanding of developments in news recommendation algorithms.  ...  Acknowledgment: This work is partially sponsored by Natural Science and Engineering Research Council of Canada (grant 2020-04760).  ... 
arXiv:2009.04964v4 fatcat:s7jl63nwm5e55myezsxpzquuje

Streaming Recommender Systems

Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.  ...  In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests.  ...  Further, we conduct experiments on three real-world datasets and sRec significantly outperforms other state-of-the-arts. It provides us an encouraging feedback to model data as streams.  ... 
doi:10.1145/3038912.3052627 dblp:conf/www/ChangZTYCHH17 fatcat:oyycxm7q6fdnnghinwpxtjbkn4

Survey on Recommender System Using Deep Learning Networks

Sushma Jaiswal, Tarun Jaiswal
2020 Artificial Intelligence Evolution  
In this paper, the Recommender system based on deep learning is proposed, and also discussed the challenges and issues which are related to the deep learning based recommender system. i.e., Accuracy, Cold  ...  There is a deferred method of the recommended system which has its own advantages and disadvantages.  ...  The envisioned hybrid music recommender beats the predictions associated with an old-fashioned content-based recommender.  ... 
doi:10.37256/aie.122020435 fatcat:maulu6rrvjepvp4ahdirbgtu7i

Knowledge Extraction And Representation Learning For Music Recommendation And Classification

Sergio Oramas, Xavier Serra
2017 Zenodo  
In this thesis, we address the problems of classifying and recommending music present in large collections.  ...  Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification  ...  Comparison with other methods We compared our approach with several state-of-the-art recommendation algorithms.  ... 
doi:10.5281/zenodo.1100973 fatcat:yfpmc6qxbbakjp6qzvywyoaoci

Knowledge Extraction And Representation Learning For Music Recommendation And Classification

Sergio Oramas, Xavier Serra
2017 Zenodo  
In this thesis, we address the problems of classifying and recommending music present in large collections.  ...  Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification  ...  Comparison with other methods We compared our approach with several state of the art recommendation algorithms.  ... 
doi:10.5281/zenodo.1048497 fatcat:kdh5jhvocbh3riwln6n2f756su

Recommender systems

Linyuan Lü, Matúš Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, Tao Zhou
2012 Physics reports  
In this article, we review recent developments in recommender systems and discuss the major challenges.  ...  The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information.  ...  Acknowledgments This work was partially supported by the EU FET-Open Grant 231200 (project QLectives) and National Natural Science Foundation of China (Grant Nos. 11075031, 11105024, 61103109 and 60973069  ... 
doi:10.1016/j.physrep.2012.02.006 fatcat:cywkeyu2wjdzhdqkg2545v5f7a

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation [article]

Ivan Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma
2021 arXiv   pre-print
This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics  ...  Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on  ...  Acknowledgement We appreciate the financial support of Spanish State Research Agency -Ministry of Science and Innovation Grant PID2019-103880RB-I00.  ... 
arXiv:2007.16120v3 fatcat:ly6d45grijbpjbk7ubm4hj3yyq

Deep Coevolutionary Network: Embedding User and Item Features for Recommendation [article]

Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song
2017 arXiv   pre-print
state-of-the-arts parametric models.  ...  Recommender systems often use latent features to explain the behaviors of users and capture the properties of items.  ...  Competitors We compared our D C with the following state-of-arts.  ... 
arXiv:1609.03675v4 fatcat:oelrk3hbkre7xhij2rigr6y44m

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Longbing Cao
2016 Engineering  
nature of recommendation problems, from the perspective of both couplings and heterogeneity.  ...  In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic  ...  This four-table view is fundamentally different from the state-of-the-art view of recommendation, which has been built on three tables (Tables A-C) only.  ... 
doi:10.1016/j.eng.2016.02.013 fatcat:odvhkmewgfewjmwcylz5wsbvii
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