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When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach [article]

Xiangkun Yin, Yangyang Guo, Liqiang Nie, Zhiyong Cheng
2021 arXiv   pre-print
To close the gap between collaborative filtering and product search, we propose a Hierarchical Heterogeneous Graph Neural Network (HHGNN) approach in this paper.  ...  In fact, the collaborative filtering signals between users intuitively offer a complementary information for the semantic matching.  ...  And the interaction edge provides rich collaborative filtering signals between users. normally enhanced with the user preference modeling.  ... 
arXiv:2108.07574v2 fatcat:k2fxc3bonvaxlmo2em46w5es3m

Multiplex Memory Network for Collaborative Filtering [chapter]

Xunqiang Jiang, Binbin Hu, Yuan Fang, Chuan Shi
2020 Proceedings of the 2020 SIAM International Conference on Data Mining  
Although current deep neural network-based collaborative filtering methods have achieved state-of-the-art performance in recommender systems, they still face a few major weaknesses.  ...  Inspired by recent progress in memory networks, we propose a novel multiplex memory network for collaborative filtering (MMCF).  ...  To enable fine-grained modeling, several studies at- tempt to employ memory networks to capture complex and fine-grained user-item interactions in collaborative filtering [1, 23, 24] .  ... 
doi:10.1137/1.9781611976236.11 dblp:conf/sdm/JiangH0S20 fatcat:eqasghthifaazd3bjam64bd75q

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  
data) and a term due to overfitting (additional suboptimality due to limited data).  ...  In the context of reinforcement learning with partial observability, this paper provides an analysis of the tradeoff between these two error sources.  ...  Li et al. proposed a general deep collaborative filtering (DCF) framework, which unifies the deep learning models with matrix factorization based collaborative filtering [Li et al., 2015a] .  ... 
doi:10.24963/ijcai.2020/695 dblp:conf/ijcai/0001Z20 fatcat:yx2wihhuobgmjjh4aevkbr33g4

Graph Meta Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo
2021 arXiv   pre-print
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.  ...  In practical recommendation scenarios, users often exhibit various intents which drive them to interact with items with multiple behavior types (e.g., click, tag-as-favorite, purchase).  ...  Autoencoder and its model variants have also been utilized for collaborative filtering tasks with the reconstruction-based encoderdecoder learning over user-item interactions [25, 34] .  ... 
arXiv:2110.03969v1 fatcat:w4mrfiyvabgsvhcu7jzototitu

Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems [article]

Yizi Zhang, Meimei Liu
2021 arXiv   pre-print
A hierarchical latent space model is utilized to learn the personalized item embedding for a given user, along with the population distribution of all user subgraphs.  ...  In this work, we introduce an exploitation-exploration motivated variational auto-encoder (XploVAE) to collaborative filtering.  ...  One of the most popular approaches is collaborative filtering (CF) [28, 29] that aims to model the users' preferences on items based on their previous interactions (e.g., ratings, clicks, purchases).  ... 
arXiv:2006.03573v4 fatcat:4jeixnidqna7be27e67qzwri6i

Price-aware Recommendation with Graph Convolutional Networks [article]

Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin
2020 arXiv   pre-print
In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g.  ...  For the second difficulty, we further integrate item categories into the propagation progress and model the possible pairwise interactions for predicting user-item interactions.  ...  Furthermore, modeling the dynamic of price is also a promising future direction.  ... 
arXiv:2003.03975v1 fatcat:s67v6cqifjbnzpzqznm6nhdgoe

Time-weighted Attentional Session-Aware Recommender System [article]

Mei Wang, Weizhi Li, Yan Yan
2019 arXiv   pre-print
And then, our ASARS framework promotes two novel models: (1) an inter-session temporal dynamic model that captures the long-term user interaction for RNN recommender system.  ...  While traditional Collaborative Filtering (CF) methods have many advanced research works on exploring long-term dependency, which show great value to be explored and exploited in deep learning models.  ...  Tims-LSTM [60] equips LSTM with time gates to model time intervals.  ... 
arXiv:1909.05414v1 fatcat:wprgje6jsbh33a4djewf3r5qda

A Systematic Study on a Customer's Next-Items Recommendation Techniques

Qazi Mudassar Ilyas, Abid Mehmood, Ashfaq Ahmad, Muneer Ahmad
2022 Sustainability  
A customer's next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit.  ...  and evaluating these systems.  ...  Collaborative Filtering Generally, collaborative filtering (CF) methods forecast the user's preferences based on previous interactions between users and items.  ... 
doi:10.3390/su14127175 fatcat:fydrheys3zd4fesibokorcprqq

Try This Instead: Personalized and Interpretable Substitute Recommendation [article]

Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, Meng Wang
2020 arXiv   pre-print
Instead of directly modelling user-item interactions, we extract explicit and polarized item attributes from user reviews with sentiment analysis, whereafter the representations of attributes, users, and  ...  In this paper, we propose attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives.  ...  DP190101985, DP170103954 and FT200100825) and National Natural Science Foundation of China (Grant No. NSFC 61806035, U1936217, 61732008 and 61725203).  ... 
arXiv:2005.09344v1 fatcat:uyr7sipndbambe7yb2anuye6fq

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation [article]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang
2020 arXiv   pre-print
Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding  ...  Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations.  ...  The core of recommender system is to predict whether a user will interact with an item, e.g., click, rate, purchase, among other forms of interactions.  ... 
arXiv:2002.02126v4 fatcat:ku7khb64wnafvlezaq34d4nhdq

SDM: Sequential Deep Matching Model for Online Large-scale Recommender System [article]

Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, Wilfred Ng
2019 arXiv   pre-print
Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry.  ...  However, they are not effective to model dynamic and evolving preferences of users.  ...  Currently, online deployed matching models at Taobao are mainly based on item-based Collaborative Filtering (CF) methods [16, 22] .  ... 
arXiv:1909.00385v2 fatcat:dyrojo7dvzbqjfp2bmfxfwmplu

Design intelligence of web application for Internet direct consumer-to-consumer trading

Vincent Chan, Tony Hao Wu, Grantham Pang
2015 2015 International Conference on Information Networking (ICOIN)  
The items for trading include books, household items, electronics, housing rental, sports equipment and tutoring services.  ...  In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns.  ...  are added to collaborative-based methods. • The collaborative-based characteristics are added to content-based methods. • Research into developing a general model that unifies both content-based and collaborative-based  ... 
doi:10.1109/icoin.2015.7057888 dblp:conf/icoin/ChanWP15 fatcat:kmakg35if5dhxjzdp23drlmu2a

Cyber Physical Recommender Systems for IoT Based Applications

Anagha Neelkanth Chaudhari
2020 American Journal of Science, Engineering and Technology  
Cyber-physical systems (CPS) are often characterized as smart systems, which intelligently interact with other systems across information and physical interfaces.  ...  Recommender systems in CPS, which always provide information recommendations for users based on historical ratings collected from a single domain only, suffer from the data sparsity problem.  ...  Going in details of methods of collaborative filtering we can distinguish most popular approaches: user-based, item-based and model-based approaches. 1.  ... 
doi:10.11648/j.ajset.20200502.14 fatcat:ynspwzes3vf7bo7bxyhqzslu5i

Space Connection: A New 3D Tele-immersion Platform for Web-Based Gesture-Collaborative Games and Services

Chun-Han Lin, Pei-Yu Sun, Fang Yu
2015 2015 IEEE/ACM 4th International Workshop on Games and Software Engineering  
Technically, to develop gesture-interactive applications, it requires parsing signals of motion sensing devices, passing network data transformation, and synchronizing states among multiple users.  ...  To realize web-based 3D immersion techniques, we propose Space Connection that integrates techniques for virtual collaboration and motion sensing techniques with the aim of pushing motion sensing a step  ...  Users use their body gestures to interact with the TV. And (4) Samsung Galaxy S4 has eye tracking camera embedded.  ... 
doi:10.1109/gas.2015.12 dblp:conf/icse/LinSY15 fatcat:6lztpugxhjffti6q2c5td4rucu

Joint Variational Autoencoders for Recommendation with Implicit Feedback [article]

Bahare Askari, Jaroslaw Szlichta, Amirali Salehi-Abari
2020 arXiv   pre-print
Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback.  ...  These existing recommendation models learn user representations to reconstruct or predict user preferences.  ...  The model learns item features through a feed-forward neural network with embeddings.  ... 
arXiv:2008.07577v1 fatcat:imcjmb4kmza7fbrt2w7iukkrx4
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