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

Dimitrios Rafailidis
2019 arXiv   pre-print
The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains.  ...  We formulate our objective function as a ranking problem in factorization machines and learn the model's parameters via gradient descent.  ...  We measured the quality of the top-n recommendations in terms of the rankingbased metrics recall and Normalized Discounted Cumulative Gain (NDCG@n).  ... 
arXiv:1908.06169v1 fatcat:xvblxtn4cjalzafcuk6e67jo2a

Cross-domain User Preference Learning for Cold-start Recommendation [article]

Huiling Zhou, Jie Liu, Zhikang Li, Jin Yu, Hongxia Yang
2021 arXiv   pre-print
Existing works mainly focus on solving either cross-domain user recommendation or cold-start content recommendation.  ...  Cross-domain cold-start recommendation is an increasingly emerging issue for recommender systems.  ...  Clustering-based approaches offer an alternative to traditional model-based methods and cluster similar users or items together [31, 40] .  ... 
arXiv:2112.03667v1 fatcat:fvg2amg5qber7encbsxgosxgtu

Contrastive Cross-domain Recommendation in Matching [article]

Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, Leyu Lin
2021 arXiv   pre-print
The intra-CL enables more effective and balanced training inside the target domain via a graph augmentation, while the inter-CL builds different types of cross-domain interactions from user, taxonomy,  ...  Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems.  ...  METHODOLOGY In this work, we propose CCDR to enhance the cross-domain recommendation in matching via contrastive learning.  ... 
arXiv:2112.00999v1 fatcat:xyvril74izggtm5x5tha32upvu

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

Ali Mamdouh Elkahky, Yang Song, Xiaodong He
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15  
We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized.  ...  The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent  ...  Recently, there has been an increasing interest in cross domain recommendation. There are different approaches for addressing cross domain recommendation.  ... 
doi:10.1145/2736277.2741667 dblp:conf/www/ElkahkySH15 fatcat:dbvcoir2qngppc2kqdtxk4kuqi

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

Xu Yu, Jun-yu Lin, Feng Jiang, Jun-wei Du, Ji-zhong Han
2018 Computational Intelligence and Neuroscience  
Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem.  ...  To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR).  ...  the set of top users ( neighbors) that are most similar to user who rated item .  ... 
doi:10.1155/2018/1425365 pmid:29623088 pmcid:PMC5830279 fatcat:6rdcuhibfja6dffk5imfu6xi3a

Cross-Domain Recommendation via Clustering on Multi-Layer Graphs

Aleksandr Farseev, Ivan Samborskii, Andrey Filchenkov, Tat-Seng Chua
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C 3 R, which utilizes both individual and group knowledge, while being trained on data from  ...  Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users' current location.  ...  [52] proposed cross-domain recommender systems, where inter-domain linking was implemented via the so-called "bridge" users (social media users who have accounts on two or more social networks).  ... 
doi:10.1145/3077136.3080774 dblp:conf/sigir/FarseevSFC17 fatcat:oq6h6njzmffj7lhzub2nsfor6a

SEEC: Semantic Vector Federation across Edge Computing Environments [article]

Shalisha Witherspoon, Dean Steuer, Graham Bent, Nirmit Desai
2020 arXiv   pre-print
A key application enabled by such techniques is the ability to measure semantic similarity between given data samples and find data most similar to a given sample.  ...  Semantic vector embedding techniques have proven useful in learning semantic representations of data across multiple domains.  ...  The second and the third columns show the top-10 most similar users returned by the joint-learned Node2vec model from each edge site with cross-site collaborators retained.  ... 
arXiv:2008.13298v1 fatcat:d4uuklh645hy3kdvbd7ws66iy4

CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network [article]

Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun
2020 arXiv   pre-print
To this end, we propose a cross-domain recommendation framework via aspect transfer network for cold-start users (named CATN).  ...  CATN is devised to extract multiple aspects for each user and each item from their review documents, and learn aspect correlations across domains with an attention mechanism.  ...  Another line of cross-domain recommender systems is clustering-based, which has also achieved good performance.  ... 
arXiv:2005.10549v1 fatcat:pmgzfsaeozgp7cjjzoz2exh7gy

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions [article]

Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, Jiadi Yu
2021 arXiv   pre-print
Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged.  ...  Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation  ...  [78] proposed a joint cross-domain user clustering and similarity learning recommendation algorithm (JCSL) in which they jointly considered cluster-based and user-based cross-domain similarities.  ... 
arXiv:2108.03357v1 fatcat:sitcklnxibafjomlq77rqvboia

Cross-domain collaboration recommendation

Jie Tang, Sen Wu, Jimeng Sun, Hang Su
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
We propose the Cross-domain Topic Learning (CTL) model to address these challenges.  ...  : cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics.  ...  Given these matrices, we can estimate the probabilities of θ, θ , ϑ, φ, and λ. Cross-domain recommendation via random walk.  ... 
doi:10.1145/2339530.2339730 dblp:conf/kdd/TangWSS12 fatcat:xv7kgpqrl5havjla5qshlzswoa

A Universal Model for Cross Modality Mapping by Relational Reasoning [article]

Zun Li, Congyan Lang, Liqian Liang, Tao Wang, Songhe Feng, Jun Wu, Yidong Li
2021 arXiv   pre-print
Then RR-Net updates all the node features and edge features in an iterative manner for learning intra and inter relations simultaneously.  ...  Extensive experiments on three example tasks, i.e., image classification, social recommendation and sound recognition, clearly demonstrate the superiority and universality of our proposed model.  ...  Cross Modality Learning Most of the existing cross modality algorithms can be classified into two categories, that is, joint embedding learning and coordinated embedding learning.  ... 
arXiv:2102.13360v1 fatcat:ng7p7cgi2zfxjldpql37udtqca

Cross domain recommendation based on multi-type media fusion

Shulong Tan, Jiajun Bu, Xuzhen Qin, Chun Chen, Deng Cai
2014 Neurocomputing  
With this model, recommendation can be done in multiple ways, via predicting ratings, comparing topic distributions of documents and user interests directly and so on.  ...  We model documents (corresponding to media objects) from different domains and user interests in a common topic space, and learn topic distributions for documents and user interests together.  ...  Cross domain recommendation Recently, some researchers introduce transfer learning methods for cross domain recommendation.  ... 
doi:10.1016/j.neucom.2013.08.034 fatcat:kkmdctp5jbf3tlkycxci62ct5u

Integrative Network Embedding via Deep Joint Reconstruction

Di Jin, Meng Ge, Liang Yang, Dongxiao He, Longbiao Wang, Weixiong Zhang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
., network community detection and user recommendation.  ...  Network embedding is to learn a low-dimensional representation for a network in order to capture intrinsic features of the network.  ...  Acknowledgments The work was supported by Natural Science Foundation of China (61502334, 61772361, 61503281), National Key R&D Program of China (2017YFC0820106) and Elite Scholar Program of Tianjin University  ... 
doi:10.24963/ijcai.2018/473 dblp:conf/ijcai/JinG0HWZ18 fatcat:2aah5fxaurcrflwyuttqsobbcy

Syntactically-Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction

Wenya Wang, Sinno Jialin Pan
2019 Computational Linguistics  
Furthermore, we construct transferable recursive neural networks to automatically learn the domain-invariant fine-grained interactions among aspect words and opinion words.  ...  In fine-grained opinion mining, extracting aspect terms (a.k.a. opinion targets) and opinion terms (a.k.a. opinion expressions) from user-generated texts is the most fundamental task in order to generate  ...  Acknowledgments This work was supported by NTU Singapore Nanyang Assistant Professorship (NAP) grant M4081532.020, Singapore MOE AcRF Tier-2 grant MOE2016-T2-2-060, and a Singapore Lee Kuan Yew Postdoctoral  ... 
doi:10.1162/coli_a_00362 fatcat:topw3vnee5ao7aevhc6sd7axdq

Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint [article]

Zhuojian Xiao, Yunjiang jiang, Guoyu Tang, Lin Liu, Sulong Xu, Yun Xiao, Weipeng Yan
2021 arXiv   pre-print
In addition, soft gating constraints based on the categorical hierarchy are imposed to help similar products choose similar gate values. and make them more likely to share similar experts.  ...  In particular, our gate network relies solely on the category ids extracted from the user query.  ...  Applications in content recommendation domains such as [18] do not involve the user query, which is a key input feature in our models.  ... 
arXiv:2007.12349v3 fatcat:6jlh7y4at5hh5b6wue2rzgt6pu
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