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Embedding Factorization Models for Jointly Recommending Items and User Generated Lists
2017
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17
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We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list ...
Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. ...
CONCLUSION AND FUTURE WORK is paper presents novel embedding factorization models for jointly recommending user generated lists and their contained items. ...
doi:10.1145/3077136.3080779
dblp:conf/sigir/CaoN0WZC17
fatcat:kpfuipswajhyxbykoac7jhgvgq
Recommendation with Multi-Source Heterogeneous Information
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. ...
Specifically, we combine item structure, textual content and tag information for recommendation. ...
Acknowledgments We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Key ...
doi:10.24963/ijcai.2018/469
dblp:conf/ijcai/GaoYWZLH18
fatcat:63fvrois7ffsfk2ojwp3yeymly
DBRec
2019
Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19
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2019 pre-print arXiv:1909.12301v1 fatcat:jzpst5jzyjgkthf7bcdxwug6ryOther Versions
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. ...
In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. ...
For example, NeuMF [10] combines generalized matrix factorization and multiple-layer perceptron for modeling user-item similarities. ...
doi:10.1145/3357384.3357892
dblp:conf/cikm/MaWZLLCYT019
fatcat:5b6hfujnbja2db4jraeam3omaa
End-to-End Image-Based Fashion Recommendation
[article]
2022
arXiv
pre-print
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to matrix factorization, auto-encoders, and nearest neighbor models. ...
In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited ...
Acknowledgements This work is co-funded by the industry Project "IIP-Ecosphere: Next Level Ecosphere for Intelligent Industrial Production". ...
arXiv:2205.02923v1
fatcat:lnjf4kjh7zemzd2bmvj6anj6ri
CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. ...
In reality, users/items are related with various couplings existing within and between users and items, which may better ex- plain how and why a user has personalized pref- erence on an item. ...
Figures 4 and 5, CoupledCF generally outperforms other versions for top-K item recommendations on both datasets. ...
doi:10.24963/ijcai.2018/509
dblp:conf/ijcai/ZhangCZLS18
fatcat:j56loxrh3bae5jc5fh4lbkz74q
Deep Content-User Embedding Model for Music Recommendation
[article]
2018
arXiv
pre-print
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In this work, we propose deep content-user embedding model, a simple and intuitive architecture that combines the user-item interaction and music audio content. ...
We evaluate the model on music recommendation and music auto-tagging tasks. The results show that the proposed model significantly outperforms the previous work. ...
The item factor, predicted item factor and item feature vector from the WMF, WMF+Regression and Deep Content-User Embedding Model is used as a feature vector of a song. ...
arXiv:1807.06786v1
fatcat:djezbsohvjfn3b5fta2qyt3ywy
Context-Aware Co-attention Neural Network for Service Recommendations
2019
2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)
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To better leverage the information from reviews, we propose an embedding method, named Entity2Vec, to jointly learn embeddings of different entities (users, items and contexts) with words in a textual ...
of users, items, and contexts. ...
preferences and items' context-aware aspects. • We also propose a novel entity embedding method to jointly learn various entities' embeddings from user generated reviews. • We conduct extensive experiments ...
doi:10.1109/icdew.2019.00-11
dblp:conf/icde/LiDC19
fatcat:6iuq5zni7bc6pltewmcdp6aab4
MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. ...
However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ...
This work is partially funded by the Social Urban Data Lab (SUDL) of the Amsterdam Institute for Advanced Metropolitan Solutions (AMS). ...
doi:10.24963/ijcai.2017/391
dblp:conf/ijcai/SunYZBCX17
fatcat:oriu4krim5bstcr2icyadibldy
Wide & Deep Learning for Recommender Systems
[article]
2016
arXiv
pre-print
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However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. ...
In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. ...
Embedding-based models, such as factorization machines [5] or deep neural networks, can generalize to previously unseen query-item feature pairs by learning a low-dimensional dense embedding vector for ...
arXiv:1606.07792v1
fatcat:dlsnlytmhjboxga7qorwtu5feu
NEAR
2019
Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization - UMAP'19 Adjunct
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high-dimensional latent factor models. ...
Many explainable recommender systems construct explanations of the recommendations these models produce, but it continues to be a difficult problem to explain to a user why an item was recommended by these ...
The training phase starts by disturbing the user embeddings which are the input to the factorisation model. ...
doi:10.1145/3314183.3323457
dblp:conf/um/OuyangL19
fatcat:z5lgvnomfbchhm26avvgjt5rt4
A Generic Framework for Learning Explicit and Implicit User-item Couplings in Recommendation
2019
IEEE Access
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User/item side information contains of attribute-based and feature-based. For different user/item side information, we use different embedding methods to learn embedding representation. ...
with attribute-based user/item information for rating prediction. (3) two datasets from Amazon Movies and TV (AMT) and Yelp for feature-based user/item information for Top-K item recommendation and rating ...
TABLE 11 . 11 Case study for recommendation list given a user on MovieLens1M.
TABLE 12 . 12 Case study for recommendation list given a user on Yelp. ...
doi:10.1109/access.2019.2937841
fatcat:q5gd75x64rc35n47dlbg4k33wi
A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
[article]
2020
arXiv
pre-print
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We conduct extensive offline evaluation of our system and demonstrate a 9.4% uplift in prediction metrics over baseline state-of-the-art within-basket recommendation models. ...
In production, our system has resulted in an increase in average basket size, improved product discovery, and enabled faster user check-out ...
The word2vec inspired CoFactor [6] model utilizes both Matrix Factorization (MF) and item embeddings jointly to generate recommendations. ...
arXiv:1910.12757v3
fatcat:azhtn2kd7vda3b2t7skgctmoie
Sense-Based Topic Word Embedding Model for Item Recommendation
2019
IEEE Access
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As a useful way to help users filter information and save time, item recommendation intends to recommend new items to users who tend to be interested. ...
To address the problems of short text feature extraction and item recommendation, we introduce a sense-based word embedding method to enrich word features and aid in item topic extraction. ...
the position of the first successfully predicted item in the recommendation list returned for the i-th user. ...
doi:10.1109/access.2019.2909578
fatcat:wg72fh7fl5bmre2n7akd24scem
Neural content-aware collaborative filtering for cold-start music recommendation
[article]
2021
arXiv
pre-print
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We propose a generative model which leverages deep learning for both extracting content information from low-level acoustic features and for modeling the interaction between users and songs embeddings. ...
However, methods falling in this category rely on a shallow user/item interaction that originates from a matrix factorization framework. ...
This allows for jointly learning the user/item embeddings and the deep content feature extractor. ...
arXiv:2102.12369v2
fatcat:ucr7nmbt35afhe4utlqufiozqy
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
[article]
2020
arXiv
pre-print
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The fixed vector will restrict the representation ability of the recommender model, considering the diversity of target items and users' interests. ...
In this paper, we propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation. ...
ACKNOWLEDGMENTS This work is jointly supported by National Key Research and Development Program (2018YFB1402600, 2016YFB1001000) and National Natural Science Foundation of China (U19B2038, 61772528). ...
arXiv:2005.02844v1
fatcat:j3l72eivajaplpgcyxpjdzcp5e
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