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Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation [article]

Yun He, Haochen Chen, Ziwei Zhu, James Caverlee
2019 arXiv   pre-print
We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-implicit feedback for enriching the original sparse implicit feedback dataset.  ...  This pseudo-implicit feedback is ultimately incorporated into a new latent factor model to estimate user preference in cases of extreme sparsity.  ...  CONCLUSIONS AND FUTURE WORK We propose PsiRec, a user preference propagation recommender designed to alleviate the data sparsity problem in top-K recommendation for implicit feedback datasets.  ... 
arXiv:1901.00597v1 fatcat:tvtvsfxljjdw7nph6juryxtqfu

Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph [article]

Riku Togashi, Mayu Otani, Shin'ichi Satoh
2020 arXiv   pre-print
We propose a knowledge graph (KG)-aware recommender based on graph neural networks, which augments labelled samples through pseudo-labelling.  ...  recommendation performance for cold-start users/items.  ...  For all datasets, we keep all the observed samples as implicit feedback.  ... 
arXiv:2011.05061v1 fatcat:3xelswj5zrfq5n72ozydj3dxuq

Multiple Attribute Aware Personalized Ranking [chapter]

Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan
2015 Lecture Notes in Computer Science  
., explicit ratings, implicit feedbacks, and multi-type attributes (such as age, sex, occupation, or posts of user).  ...  Personalized ranking is a typical task of recommender systems. It can provide a set of items for specific user and help recommender systems more correctly direct each item to its user.  ...  ., iMF, can be extended to deal with implicit feedbacks, the phenomenon of data skew commonly exists in the datasets of implicit feedbacks (the number of positive samples is usually less than one percent  ... 
doi:10.1007/978-3-319-25255-1_20 fatcat:r3th5sxpfrhjlngfxchqqckdju

A Neural Influence Diffusion Model for Social Recommendation [article]

Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
2019 arXiv   pre-print
With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding  ...  In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation.  ...  Performance under Different Data Sparsity The data sparsity issue is a main challenge for most CF based recommender systems.  ... 
arXiv:1904.10322v1 fatcat:hyo2gnvjznejheoc63hojpor3q

SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation [article]

Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang
2019 arXiv   pre-print
Most of these social recommendation models utilized each user's local neighbors' preferences to alleviate the data sparsity issue in CF.  ...  To this end, in this paper, we propose an effective graph convolutional neural network based model for social recommendation.  ...  The social recommender systems have emerged as a promising direction, which leverage the social network among users to alleviate the data sparsity issue and improve recommendation performance (Ma et al  ... 
arXiv:1811.02815v2 fatcat:rg7ak7rywfbjxlljcfdrdcvhsa

Personalized news recommendation via implicit social experts

Chen Lin, Runquan Xie, Xinjun Guan, Lei Li, Tao Li
2014 Information Sciences  
Hybrid approaches, which alleviate drawback of individual recommendation strategy, provide solutions to data sparsity problem.  ...  We propose PRemiSE, a novel Personalized news Recommendation framework via implicit Social Experts, in which the opinions of potential influencers on virtual social networks extracted from implicit feedbacks  ...  Alternatives of collaborative filtering methods are presented to alleviate the data sparsity problem, but they all have serious drawbacks.  ... 
doi:10.1016/j.ins.2013.08.034 fatcat:lrx7gvofavhotiohm2xfr4rw2i

A Social Collaborative Filtering Method to Alleviate Data Sparsity Based on Graph Convolutional Networks

Haitao XIE, Qingtao FAN, Qian XIAO
2020 IEICE transactions on information and systems  
The experiments on real datasets show that our approach outperforms the other state-of-the-art methods on usage of multi-source data to alleviate data sparsity.  ...  To overcome the inherent limitation, which is referred to as data sparsity in CF, various solutions are proposed to incorporate additional social information into recommendation processes, such as trust  ...  Effect of Alleviating Data Sparsity The target of this research is trying to alleviate data sparsity by incorporating additional social information.  ... 
doi:10.1587/transinf.2019edp7286 fatcat:lgmymhekpvb7jcxlbcjua3tbgm

Collaborative Distillation for Top-N Recommendation [article]

Jae-woong Lee, Minjin Choi, Jongwuk Lee, Hyunjung Shim
2019 arXiv   pre-print
Despite the success of KD in the classification task, applying KD to recommender models is challenging due to the sparsity of positive feedback, the ambiguity of missing feedback, and the ranking problem  ...  associated with the top-N recommendation.  ...  In this sense, RD regards the knowledge transferred from the teacher model as augmented positive feedback, which helps alleviate the data sparsity problem associated with top-N recommendation.  ... 
arXiv:1911.05276v1 fatcat:bymyvyvsnvgo3ckeqcczgdw3ji

SamWalker++: recommendation with informative sampling strategy [article]

Can Wang, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen
2021 arXiv   pre-print
Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data.  ...  However, since a user is only aware of a very small fraction of items in a large dataset, it is difficult for existing samplers to select informative training instances in which the user really dislikes  ...  For each user i, we define Rec(i) as the set of recommended items in top-K and Con(i) as the set of consumed items in test data for user i.  ... 
arXiv:2011.07734v2 fatcat:viztewi3hvhpnoklxvbqvd62we

Fusing User Reviews Into Heterogeneous Information Network Recommendation Model

Xu Chen, Jingjing Tian, Xinxin Tian, Shudong Liu
2022 IEEE Access  
However, most of the current recommendation models fail to make the most of the ample resources hidden behind auxiliary data and user reviews. For this reason, we put forward the FHRec model.  ...  With the advent of the information epoch and the development of Big Data, users are constantly overwhelmed in massive information online.  ...  In addition, we also add Recall@K, NDCG@K, HR@K and AUC as metrics to evaluate the Top-K recommendation list generated by the proposed recommendation algorithm.  ... 
doi:10.1109/access.2022.3176727 fatcat:n5s435ezezfgffjkcinc4oug5u

Bilateral Self-unbiased Learning from Biased Implicit Feedback [article]

Jae-woong Lee, Seongmin Park, Joonseok Lee, Jongwuk Lee
2022 arXiv   pre-print
Implicit feedback has been widely used to build commercial recommender systems.  ...  In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models.  ...  For implicit feedback data, the user interaction is a result of observation and preference.  ... 
arXiv:2207.12660v1 fatcat:lsxnmawzdrfzri3eooig3ikr7e

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [article]

Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang
2021 arXiv   pre-print
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation  ...  relationships for each user.  ...  In summary, these models leveraged the first-order social neighbors for recommendation, and partially alleviated the data sparsity issue in CF.  ... 
arXiv:2002.00844v4 fatcat:c3ru4lxozfef7hovzgwojrxqt4

A Hierarchical Attention Model for Social Contextual Image Recommendation [article]

Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang
2019 arXiv   pre-print
To this end, in this paper, we develop a hierarchical attention model for social contextual image recommendation.  ...  In fact, many hybrid models have been proposed to fuse various kinds of side information~(e.g., image visual representation, social network) and user-item historical behavior for enhancing recommendation  ...  On the other hand, as users perform image preferences in social platforms, some social based recommendation algorithms utilized the social influence among users to alleviate data sparsity for better recommendation  ... 
arXiv:1806.00723v3 fatcat:3jc5twhvyvbevhec5zpzjlhvwy

A Hybrid Distributed Collaborative Filtering Recommender Engine Using Apache Spark

Sasmita Panigrahi, Rakesh Ku. Lenka, Ananya Stitipragyan
2016 Procedia Computer Science  
In the big data world, recommendation system is becoming growingly popular.  ...  Dimensionality reduction techniques like Alternating Least Square and Clustering techniques like K-Means are used in order to overcome the limitations of Collaborative Filtering such as data Sparsity and  ...  Introduction In the big data world, recommendation system is becoming growingly popular.  ... 
doi:10.1016/j.procs.2016.04.214 fatcat:ybt5ynv4mjd7fe3k2cj7b4oyeq

Mixed Similarity Diffusion for Recommendation on Bipartite Networks

Ximeng Wang, Yun Liu, Guangquan Zhang, Yi Zhang, Hongshu Chen, Jie Lu
2017 IEEE Access  
data, which would be a significant feature in recommender systems.  ...  In recommender systems, collaborative filtering technology is an important method to evaluate user preference through exploiting user feedback data, and has been widely used in industrial areas.  ...  Its main idea is to evaluate user preference through exploiting user feedback data in a collective way. Two kinds of feedback data can be processed, i.e., explicit feedback and implicit feedback.  ... 
doi:10.1109/access.2017.2753818 fatcat:3itknypstfalxbfk2df6fc5eqm
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