Cross-Domain Collaborative Filtering with Factorization Machines.

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

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

DOAJ

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

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

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

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

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

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

DOAJ

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

[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

Open Access

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

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

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

Open Access

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

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

Cross-Domain Collaborative Filtering with Factorization Machines [chapter]

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Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

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Factorization Machines for Data with Implicit Feedback [article]

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Graph Factorization Machines for Cross-Domain Recommendation [article]

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Enhanced Cross Domain Recommender System using Contextual parameters in Temporal Domain

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

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

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

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

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An Application-oriented Review of Deep Learning in Recommender Systems

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Divide and Transfer: Understanding Latent Factors for Recommendation Tasks

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

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"Major Challenges of Recommender System and Related Solutions"

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Editorial: Cognitive Industrial Internet of Things

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Deep Collaborative Filtering via Marginalized Denoising Auto-encoder

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