• Publications
  • Influence
FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems
Recommender systems have many successful applications in e-commerce and social media, including Amazon, Netflix, and Yelp. Matrix Factorization (MF) is one of the most popular recommendationExpand
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On Multi-Relational Link Prediction with Bilinear Models
We study bilinear embedding models for the task of multi-relational link prediction and knowledge graph completion. Bilinear models belong to the most basic models for this task, they are comparablyExpand
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TransRev: Modeling Reviews as Translations from Users to Items
The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review scoreExpand
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HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation
Location-based social networks such as Foursquare and Plancast have gained increasing popularity. On those sites, users can organize and participate in group activities; hence, recommending venues toExpand
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Overlapping Community Regularization for Rating Prediction in Social Recommender Systems
Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user's rating on an item is the fundamental recommendation task. TraditionalExpand
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Content-Based Filtering Recommendation Algorithm Using HMM
In this paper, we combine probabilistic model and classical content-based filtering recommendation algorithms to propose a new algorithm for recommendation system, which we call content-basedExpand
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MMKG: Multi-Modal Knowledge Graphs
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore,Expand
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Efficient Fault-Tolerant Group Recommendation Using alpha-beta-core
Fault-tolerant group recommendation systems based on subspace clustering successfully alleviate high-dimensionality and sparsity problems. However, the cost of recommendation grows exponentially withExpand
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RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems
  • Hui Li, Mathias Niepert, Hui Li
  • Computer Science, Medicine
  • IEEE Transactions on Neural Networks and Learning…
  • 26 March 2019
Data sparsity and data imbalance are practical and challenging issues in cross-domain recommender systems (RSs). This paper addresses those problems by leveraging the concepts which derive fromExpand
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HHMF: hidden hierarchical matrix factorization for recommender systems
Matrix factorization (MF) is one of the most powerful techniques used in recommender systems. MF models the (user, item) interactions behind historical explicit or implicit ratings. Standard MF doesExpand
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