47,933 Hits in 4.5 sec

Cross-Domain Collaborative Filtering with Factorization Machines [chapter]

Babak Loni, Yue Shi, Martha Larson, Alan Hanjalic
2014 Lecture Notes in Computer Science  
Factorization machines offer an advantage over other existing collaborative filtering approaches to recommendation.  ...  Our proposed approach is tested on a data set from Amazon and compared with a state-of-the-art approach that has been proposed for Cross-Domain Collaborative Filtering.  ...  Related Work Cross-Domain Collaborative Filtering: An overview of CDCF approaches is available in Li [9] .  ... 
doi:10.1007/978-3-319-06028-6_72 fatcat:2dt275yawzcgrhddy5q7ol4beq

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Nor Aniza Abdullah, Rasheed Abubakar Rasheed, Mohd Hairul Nizam Md. Nasir, Md Mujibur Rahman
2021 Applied Sciences  
Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build  ...  educational institutions and academia, or with cold start for mobile applications.  ...  These approaches involve improving filtering strategies such as collaborative filtering or matrix factorization; or collaborative filtering and matrix factorization coupled with machine learning algorithms  ... 
doi:10.3390/app11209608 fatcat:foxbu3gt4fdxhdxuhijtufkyiu

Factorization Machines for Data with Implicit Feedback [article]

Babak Loni, Martha Larson, Alan Hanjalic
2018 arXiv   pre-print
We also propose how to apply FM-Pair effectively on two collaborative filtering problems, namely, context-aware recommendation and cross-domain collaborative filtering.  ...  In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback.  ...  Factorization Machines have been successfully applied in different collaborative filtering problems including context-aware recommendation [Rendle et al. 2011 ], cross-domain collaborative filtering ]  ... 
arXiv:1812.08254v1 fatcat:krbtdxyx6jeghho3ijchwvpj4a

Graph Factorization Machines for Cross-Domain Recommendation [article]

Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu, Pengpeng Zhao, Xiangliang Zhang, Qing He
2020 arXiv   pre-print
Besides, based on general cross-domain recommendation experiments, we also demonstrate that our cross-domain framework could not only contribute to the cross-domain recommendation task with the GFM, but  ...  In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation.  ...  In order to tackle the above problem, we propose a novel Graph Factorization Machine (GFM) with the advantage of popular Factorization Machine (FM) [9] .  ... 
arXiv:2007.05911v1 fatcat:f6xugvw5ifglzeprw542gad72u

Enhanced Cross Domain Recommender System using Contextual parameters in Temporal Domain

Swapna Joshi, Prof. Manisha Patil
2017 International Journal of Computer Applications Technology and Research  
Cross-domain collaborative filtering (CDCF) is an evolving research topic in the modern recommender systems.  ...  It also deals with cross domain recommendations for both movies and novels based on their categories and similarities.  ...  Cross-domain recommender systems [1] Yes No Cross-domain collaborative filtering over time [4] Yes No Collaborative filtering with temporal dynamics [5] Yes No A spatio-temporal approach to collaborative  ... 
doi:10.7753/ijcatr0608.1002 fatcat:ppxdvno52ffmxm25uzzy4v3zki

Comparative study on traditional recommender systems and deep learning based recommender systems

N.L. Anantha, Bhanu Bathula
2018 Advances in Modelling and Analysis B  
Now a day Deep Learning is using in every domain. Deep Learning techniques in the field of Recommender Systems can be directly applied. Deep Learning has ample number of algorithms.  ...  Cross-domain Content-boosted Collaborative Filtering Neural Network [15] is MLP based technique offers user-based and item-based recommendations.  ...  Graph lab is Machine learning platform offers Collaborative filtering, Matrix Factorization, Top N Recommendation.  ... 
doi:10.18280/ama_b.610202 fatcat:4iur3pjuujdkha6dyt3v6ntequ

Cross-Domain Collaborative Filtering via Translation-based Learning [article]

Dimitrios Rafailidis
2019 arXiv   pre-print
With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences  ...  In this paper, we propose a Cross-Domain collaborative filtering model following a Translation-based strategy, namely CDT.  ...  Representative collaborative filtering strategies are latent models such as Matrix Factorization and Factorization Machines (FMs) [1] , which factorize the data matrix with user preferences in a single  ... 
arXiv:1908.06169v1 fatcat:xvblxtn4cjalzafcuk6e67jo2a

A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-domain Recommendation

Wenxing Hong, Nannan Zheng, Ziang Xiong, Zhiqiang Hu
2020 IEEE Access  
INDEX TERMS Cross-domain recommendation, convolutional neural networks, rating prediction.  ...  In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation.  ...  A common solution to the data sparsity problem is to integrate collaborative filtering with content information to form a hybrid approach because users and items are generally associated with content information  ... 
doi:10.1109/access.2020.2977123 fatcat:4lfqtfpt4jdplf2j66do2p325y

On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)

Vincent Francois-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In the context of reinforcement learning with partial observability, this paper provides an analysis of the tradeoff between these two error sources.  ...  When an agent has limited information on its environment, the suboptimality of an RL algorithm can be decomposed into the sum of two terms: a term related to an asymptotic bias (suboptimality with unlimited  ...  [Truyen et al., 2009] proposed ordinal Boltzmann machines for collaborative filtering.  ... 
doi:10.24963/ijcai.2020/695 dblp:conf/ijcai/0001Z20 fatcat:yx2wihhuobgmjjh4aevkbr33g4

An Application-oriented Review of Deep Learning in Recommender Systems

Jyoti Shokeen, Chhavi Rana
2019 International Journal of Intelligent Systems and Applications  
[68] use deep neural networks to propose neural collaborative social ranking (NCSR) method to integrate user-item interactions from information domains and user relations from social domain for cross-domain  ...  These aspects are: tagging, communities, trusted relations, communities and cross-domain knowledge. III. DEEP LEARNING Deep learning is a subfield of machine learning.  ... 
doi:10.5815/ijisa.2019.05.06 fatcat:67fgexfbfjh2no5b3phvohbole

Divide and Transfer: Understanding Latent Factors for Recommendation Tasks

Vidyadhar Rao, Rosni K. V, Vineet Padmanabhan
2017 ACM Conference on Recommender Systems  
In this work, we propose a collaborative filtering technique that can effectively utilize the user preferences and content information.  ...  We demonstrate the effectiveness of our approach due to improved latent feature space in both single and cross-domain tasks.  ...  In addition to this, we have used a simple collaborative filtering approach with zero-rating information from target domain, we believe utilizing the target domain ratings could result in better cross-domain  ... 
dblp:conf/recsys/RaoVP17 fatcat:ynnxwwi4wnhcxb7d5oov56dise

Cross-domain recommender system using Generalized Canonical Correlation Analysis [article]

Seyed Mohammad Hashemi, Mohammad Rahmati
2019 arXiv   pre-print
Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items.  ...  Whenever a new user participate with the system there is not enough interactions with the system, therefore there are not enough ratings in the user-item matrix to learn the matrix factorization model.  ...  Many of the traditional recommender systems are based on single domain collaborative filtering.  ... 
arXiv:1909.12746v1 fatcat:lbviw5dedjfwbj3jygxicfx73a

"Major Challenges of Recommender System and Related Solutions"

Surya Naga Sai Lalitha Chirravuri, Kali Pradeep Immidi
2022 International Journal of Innovative Research in Computer Science & Technology  
The model uses an item's specifications in content-based filtering to suggest other objects with similar features.  ...  View user profile data such as age category, gender, education, and living area to detect commonalities with other profiles.[31] All three filtering techniques are used in hybrid filtering.  ...  Collaborative filtering is further classified into 2 types: user-based collaborative filtering and item-based collaborative filtering.  ... 
doi:10.55524/ijircst.2022.10.2.3 fatcat:6dmvby2gujhkzh4swaji4qt5j4

Editorial: Cognitive Industrial Internet of Things

Long Hu, Daxin Tian, Kai Lin
2018 Journal on spesial topics in mobile networks and applications  
Moreover, the sufficient experiments show that the proposed system is significantly improved compared with the conventional recommender approaches, such as collaborative filtering and matrix factorization  ...  The first article, BCrossRec: Cross-domain Recommendations based on Social Big Data and Cognitive Computing^, authored by Yin Zhang et al., considered the advantages of social-based and cross-domain approaches  ...  Moreover, the sufficient experiments show that the proposed system is significantly improved compared with the conventional recommender approaches, such as collaborative filtering and matrix factorization  ... 
doi:10.1007/s11036-018-1115-y fatcat:nimwoztjbrdnznauphzjd7r23m

Deep Collaborative Filtering via Marginalized Denoising Auto-encoder

Sheng Li, Jaya Kawale, Yun Fu
2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15  
Learning effective latent factors plays the most important role in collaborative filtering.  ...  Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems.  ...  Hu et al. proposed a cross-domain triadic factorization (CDTF) method [29] , which leverages the information from other domains.  ... 
doi:10.1145/2806416.2806527 dblp:conf/cikm/LiKF15 fatcat:v7yk4lqxyrcr7ho2ycy24ex4ia
« Previous Showing results 1 — 15 out of 47,933 results