Proceedings ArticleDOI
Nonlinear latent factorization by embedding multiple user interests
Jason Weston,Ron Weiss,Hector Yee +2 more
- pp 65-68
TLDR
This work proposes to model the user with a richer set of functions, specifically via a set of latent vectors, where each vector captures one of the user's latent interests or tastes, and describes a simple, general and efficient algorithm for learning such a model.Abstract:
Classical matrix factorization approaches to collaborative filtering learn a latent vector for each user and each item, and recommendations are scored via the similarity between two such vectors, which are of the same dimension In this work, we are motivated by the intuition that a user is a much more complicated entity than any single item, and cannot be well described by the same representation Hence, the variety of a user's interests could be better captured by a more complex representation We propose to model the user with a richer set of functions, specifically via a set of latent vectors, where each vector captures one of the user's latent interests or tastes The overall recommendation model is then nonlinear where the matching score between a user and a given item is the maximum matching score over each of the user's latent interests with respect to the item's latent representation We describe a simple, general and efficient algorithm for learning such a model, and apply it to large scale, real-world datasets from YouTube and Google Music, where our approach outperforms existing techniquesread more
Citations
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Proceedings ArticleDOI
Learning Semantic Representations of Users and Products for Document Level Sentiment Classification
Duyu Tang,Bing Qin,Ting Liu +2 more
TL;DR: By combining evidence at user-, product and documentlevel in a unified neural framework, the proposed model achieves state-of-the-art performances on IMDB and Yelp datasets1.
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Graph Neural Networks in Recommender Systems: A Survey
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Real-time Personalization using Embeddings for Search Ranking at Airbnb
Mihajlo Grbovic,Haibin Cheng +1 more
TL;DR: The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest's short-term and long-term interests, delivering effective home listing recommendations.
Proceedings ArticleDOI
Compositional Vector Space Models for Knowledge Base Completion
TL;DR: In this article, the authors present an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recurrent neural network that takes as inputs vector embeddings of the binary relation in the path.
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Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Chao Li,Zhiyuan Liu,Mengmeng Wu,Yuchi Xu,Pipei Huang,Huan Zhao,Guoliang Kang,Qiwei Chen,Wei Li,Dik Lun Lee +9 more
TL;DR: This paper designs a multi-interest extractor layer based on the recently proposed dynamic routing mechanism, which is applicable for modeling and extracting diverse interests from user's behaviors, and proposes a technique named label-aware attention to help the learning process of user representations.
References
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Proceedings ArticleDOI
Restricted Boltzmann machines for collaborative filtering
TL;DR: This paper shows how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies, and demonstrates that RBM's can be successfully applied to the Netflix data set.
Book ChapterDOI
Advances in Collaborative Filtering
Yehuda Koren,Robert M. Bell +1 more
TL;DR: In this paper, the authors survey the recent progress in the field of collaborative filtering and describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field and demonstrate how to utilize temporal models and implicit feedback to extend models accuracy.
Proceedings ArticleDOI
WSABIE: scaling up to large vocabulary image annotation
TL;DR: This work proposes a strongly performing method that scales to image annotation datasets by simultaneously learning to optimize precision at the top of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both images and annotations.
Proceedings Article
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking
TL;DR: A method which uses Maximum Margin Matrix Factorization and optimizes ranking instead of rating is presented and gives very good ranking scores and scales well on collaborative filtering tasks.