Information Retrieval and RecommendationsInformation Retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds. Information Recommendation seeks to predict the "rating" or "preference" a user would give to an item. It is primarily used in commercial applications.
ACM Computing Surveys (CSUR), no. 1 (2019)
We provided an extensive review of the most notable works to date on deep learning based recommender system
Cited by533BibtexViews233Links
0
0
KDD, (2018): 974-983
We proposed PinSage, a random-walk graph convolutional network
Cited by423BibtexViews419Links
0
0
WWW '18: The Web Conference 2018 Lyon France April, 2018, pp.167-176, (2018)
We propose a Deep Q-Learning-based reinforcement learning framework to do online personalized news recommendation
Cited by140BibtexViews233Links
0
0
Mehrbakhsh Nilashi,Othman Ibrahim, Karamollah Bagherifard
Expert Syst. Appl., (2018): 507-520
The present study proposed a recommendation method based on Collaborative Filtering using ontology and dimensionality reduction techniques to improve the sparsity and scalability problems in Collaborative Filtering
Cited by118BibtexViews38Links
0
0
SIGIR, (2018): 355-364
We show that a model optimized by Bayesian Personalized Ranking, a dominant pairwise learning method in recommendation, is vulnerable to adversarial perturbations on its parameters
Cited by109BibtexViews64Links
1
0
Alex Beutel, Paul Covington, Sagar Jain,Can Xu, Jia Li, Vince Gatto,Ed H. Chi
WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining Marina De..., pp.46-54, (2018)
Production Model: We offer a detailed description of our recurrent neural networks-based recommender system used at YouTube
Cited by105BibtexViews79Links
0
0
SIGIR, pp.505-514, (2018)
We proposed to extend the Gated Recurrent Unit-based sequential recommender by integrating it with knowledge-enhanced KV-Memory Networks
Cited by84BibtexViews182Links
0
0
IJCAI, pp.3926-3932, (2018)
We proposed a hierarchical attention network for recommending item problem
Cited by76BibtexViews86Links
0
0
SIGIR, (2018): 405-414
This paper argues for equity of attention – a new notion of fairness in rankings, which requires that the attention ranked subjects receive from searchers is proportional to their relevance. As this definition cannot be satisfied in a single ranking because of the position bias, ...
Cited by71BibtexViews67Links
0
0
KDD, (2018): 1040-1048
We propose a novel framework deep recommender system, which models the recommendation session as a Markov Decision Process and leverages Reinforcement Learning to automatically learn the optimal recommendation strategies
Cited by64BibtexViews99Links
0
0
SIGIR, (2018): 5-14
We present an attentive knowledge distillation scheme towards compatibility modeling in the context of clothing matching, which jointly learns from both the specific data samples and general knowledge rules
Cited by59BibtexViews82Links
0
0
SIGIR, pp.415-424, (2018)
To the best of our knowledge, these represent the first specific results to be reached on the question whether popularity is an effective or misleading signal in recommendation –and the first to suggest the average rating might be preferable to the number of favorable preferences...
Cited by30BibtexViews37Links
0
0
Lei Zheng, Vahid Noroozi,Philip S. Yu
WSDM, (2017)
We presented Deep Cooperative Neural Networks which exploits the information exists in the reviews for recommender systems
Cited by359BibtexViews148Links
0
0
WSDM, pp.495-503, (2017)
We have provided Recommender Networks, a novel recommender system based on recurrent neural networks that can accurately model user and movie dynamics
Cited by292BibtexViews261Links
0
0
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Informatio..., (2017): 515-524
As shown in Table 5, IRGAN outperforms both the basic convolutional neural network model with a random sampling strategy and the enhanced convolutional neural network model with a dynamic negative sampling strategy. e learning curves of the two models are shown in Figure 8, which...
Cited by286BibtexViews95Links
0
0
IJCAI, pp.3203-3209, (2017)
Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities ...
Cited by212BibtexViews55Links
0
0
Matevz Kunaver, Tomaz Pozrl
Knowl.-Based Syst., no. C (2017): 154-162
Diversification has become one of the leading topics of recommender system research not only as a way to solve the over-fitting problem but also an approach to increasing the quality of the users experience with the recommender system. This article aims to provide an overview of ...
Cited by133BibtexViews5Links
0
0
WWW, pp.391-400, (2017)
We investigate visual contents to advance traditional POI recommender systems
Cited by130BibtexViews67Links
0
0
SIGIR, (2017): 345-354
We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings
Cited by84BibtexViews25Links
0
0
SIGIR, pp.315-324, (2017)
Parameter settings. e hyper-parameters in our frameworks are tuned by conducting 5-fold cross validation, while the model parameters are rst randomly initialized according to a uniform distribution in the range of, and updated by conducting stochastic gradient descent. e learning...
Cited by70BibtexViews52Links
0
0
Keywords
Information RetrievalIndexationRecommender SystemCollaborative FilteringRecommender SystemsSearch EngineWeb Search EngineDocument RetrievalEvaluationInverted Index
Authors
Jie Tang
Paper 15
Juanzi Li
Paper 9
W. Bruce Croft
Paper 5
Torsten Suel
Paper 5
Jirong Wen
Paper 4
Ryen W. White
Paper 3
Fuzheng Zhang
Paper 3
Shuming Shi
Paper 3
Gerhard Weikum
Paper 3