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Content-aware Neural Hashing for Cold-start Recommendation [article]

Casper Hansen and Christian Hansen and Jakob Grue Simonsen and Stephen Alstrup and Christina Lioma
2020 pre-print
Content-aware recommendation approaches are essential for providing meaningful recommendations for new (i.e., cold-start) items in a recommender system.  ...  We present a content-aware neural hashing-based collaborative filtering approach (NeuHash-CF), which generates binary hash codes for users and items, such that the highly efficient Hamming distance can  ...  We present a novel neural approach for content-aware hashingbased collaborative filtering (NeuHash-CF) robust to cold-start recommendation problems.  ... 
doi:10.1145/3397271.3401060 arXiv:2006.00617v1 fatcat:b53nhnze2resnj2itki4fpxplu

Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation [article]

Yan Zhang, Ivor W. Tsang, Lixin Duan
2020 arXiv   pre-print
Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce.  ...  Most existing studies proposed to alleviate the cold-start problem are also known as hybrid recommender systems that learn representations of users and items by combining user-item interactive and user  ...  Accuracy for the Cold-start Item Recommendation.  ... 
arXiv:2011.00953v1 fatcat:l643dlp2g5gsve2pv4loir45ru

Representation Learning for Efficient and Effective Similarity Search and Recommendation [article]

Casper Hansen
2021 arXiv   pre-print
State of the art methods use representation learning for generating such hash codes, focusing on neural autoencoder architectures where semantics are encoded into the hash codes by learning to reconstruct  ...  Due to the limited expressibility of hash codes, compared to real-valued representations, a core open challenge is how to generate hash codes that well capture semantic content or latent properties using  ...  Content-aware Neural Hashing for Cold-start Recommendation Recommendation approaches based on collaborative filtering, content-based filtering, and their combinations have been well studied and shown to  ... 
arXiv:2109.01815v1 fatcat:tlq2uweeebde5gmi56ubm6rttm

Personalized Recommendation Algorithm of Smart Tourism Based on Cross-Media Big Data and Neural Network

Jing Lu, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
Aiming at the problem of sparse data and cold start of collaborative filtering recommendation algorithm, this paper introduces deep learning algorithm and combines the proposed multifeature tourism factors  ...  to build dynamic scenic spot prediction models (random forest preferred attraction prediction (RFPAP) and neural networks preferred attraction prediction (NNPAP)).  ...  content-based and context-aware information technology to associate with the user's geographical location and filters the recommendation results according to the keyword matching knowledge base, which  ... 
doi:10.1155/2022/9566766 pmid:35795765 pmcid:PMC9251073 fatcat:atcrxle7zndebndqvm4ularmxe

Neural Hybrid Recommender: Recommendation needs collaboration [article]

Ezgi Yıldırım, Payam Azad, Şule Gündüz Öğüdücü
2019 arXiv   pre-print
This framework named NHR, short for Neural Hybrid Recommender allows us to include more elaborate information from the same and different data sources.  ...  After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods.  ...  The authors would like to thank Istanbul Technical University for their financial support under the project BAP-40737.  ... 
arXiv:1909.13330v1 fatcat:a3z2hdzdovgfnmaebovus5ymui

Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline [article]

Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, Gabriele Sottocornola, Walter Anelli (+3 others)
2020 arXiv   pre-print
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception.  ...  the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content  ...  Even though this work claims that content features can be used to represent users and items to address the cold-start problem, in fact, only their corresponding identities are used as input features in  ... 
arXiv:2004.13715v3 fatcat:brejbw6t5vdg7paikgutfhkufu

CuratorNet: Visually-aware Recommendation of Art Images [article]

Pablo Messina, Manuel Cartagena, Patricio Cerda-Mardini, Felipe del Rio, Denis Parra
2020 arXiv   pre-print
To reduce this gap, in this article we introduceCuratorNet, a neural network architecture for visually-aware recommendation of art images.  ...  Although there are several visually-aware recommendation models in domains like fashion or even movies, the art domain lacks thesame level of research attention, despite the recent growth of the online  ...  [32] ) and which recommends to cold-start items and users without additional model training (unlike [19] ).  ... 
arXiv:2009.04426v2 fatcat:gtsvfijfdrghzlwfkgoqimshxu

An Application-oriented Review of Deep Learning in Recommender Systems

Jyoti Shokeen, Chhavi Rana
2019 International Journal of Intelligent Systems and Applications  
Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years.  ...  This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications.  ...  The model gives the reliable healthcare recommendations even for the cold-start users. Katzman et al.  ... 
doi:10.5815/ijisa.2019.05.06 fatcat:67fgexfbfjh2no5b3phvohbole

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

Yashar Deldjoo, Markus Schedl, Peter Knees
2021 arXiv   pre-print
context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start.  ...  The music domain is among the most important ones for adopting recommender systems technology.  ...  Cold start: As a consequence, a central challenge of recommender systems, namely the cold start problem for item information, increases towards the outer layers due to increasing data scarcity within individual  ... 
arXiv:2107.11803v1 fatcat:4hz4hqkkmvcapbdr3wvtp2t4iu

Sequential Recommendations on GitHub Repository

JaeWon Kim, JeongA Wi, YoungBin Kim
2021 Applied Sciences  
Further, predicting users' propensity in this huge community and recommending a new repository is beneficial for researchers and users.  ...  Despite this, only a few researches have been done on the recommendation system of such platforms.  ...  of the size of cold-start interactions.  ... 
doi:10.3390/app11041585 fatcat:uyei5z2vhzbzvgmq6nz75ocmie

CHAMELEON: A Deep Learning Meta-Architecture for News Recommender Systems [Phd. Thesis] [article]

Gabriel de Souza Pereira Moreira
2019 arXiv   pre-print
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start  ...  Articles' content is also important to tackle the item cold-start problem. Additionally, the temporal decay of items (articles) relevance is very accelerated in the news domain.  ...  Their proposed model especially improved recommendations at the start of sessions and was able to deal with the cold-start problem in session-aware RS.  ... 
arXiv:2001.04831v1 fatcat:x2k3u26i4jebzjlesswnncfepq

ICDIM 2018 Author Index

2018 2018 Thirteenth International Conference on Digital Information Management (ICDIM)  
Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation Akinrolabu, Olusola 426-434 Cloud Service Supplier Assessment: A Delphi Study Al Mamun,  ...  Function Ranasinghe, Tharindu 24-28 User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation Rasheed, Imran 274-278  ... 
doi:10.1109/icdim.2018.8846994 fatcat:f5l5uufqcrbrdb3xdl5id7kabe

Hierarchical Preference Hash Network for News Recommendation

Jianyong DUAN, Liangcai LI, Mei ZHANG, Hao WANG
2022 IEICE transactions on information and systems  
Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience.  ...  news recommendation.  ...  We would also like to thank the anonymous reviewers for their helpful comments. We would like to thank the referees for their comments, which helped improve this paper considerably.  ... 
doi:10.1587/transinf.2021edp7034 fatcat:sqlzfpidqrcbld2xswrtegls7i

Discrete Factorization Machines for Fast Feature-based Recommendation

Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
User and item features of side information are crucial for accurate recommendation.  ...  In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.  ...  As such, it suffers from the cold-start problem and can not be used as a generic recommendation solution, e.g., for context-aware [Rendle, 2011] and session-based recommendation [Bayer et al., 2017]  ... 
doi:10.24963/ijcai.2018/479 dblp:conf/ijcai/Liu0FNLZ18 fatcat:o4i62ez52zdqlepreyzuo7suq4

Discrete Factorization Machines for Fast Feature-based Recommendation [article]

Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang
2018 arXiv   pre-print
User and item features of side information are crucial for accurate recommendation.  ...  In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.  ...  As such, it suffers from the cold-start problem and can not be used as a generic recommendation solution, e.g., for context-aware [Rendle, 2011] and session-based recommendation [Bayer et al., 2017]  ... 
arXiv:1805.02232v3 fatcat:cwekaftc6rc4hkjut7bbxpjxom
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