诺库维普

Set Transformer: A framework for Attention-based Permutation-Invariant Neural Networks 2019-03-04 05:29:37

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few- shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. ... || self-attention...

Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks 2019-03-03 04:49:40

Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the... || Meta-Graph; path-representation; || Huan Zhao and Quanming Yao and Jianda Li and Yangqiu Song and Dik Lun Lee...

Non-local Neural Networks 2019-02-28 05:27:17

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer... || nonlocal; || Xiaolong Wang...

Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation 2019-02-26 06:08:03

Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users’ preferences in the target domain. Thought to be unrelated, u... || content; cnn; rating predition; combination; SDAE; 简单学习问题绘图; || Weizhi Ma...

Recurrent Collaborative Filtering for Unifying General and Sequential Recommender 2019-02-22 11:06:00

General recommender and sequential recommender are two applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors, whereas sequential recommender focuses on exploring the item-to-item s... || multi-task learning ; sequential (item-to-item relations); || Disheng Dong...

Improving Implicit Recommender Systems with View Data 2019-02-22 05:42:06

Most existing recommender systems leverage theprimary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed asImplicit Recommender Systems). We propose to model the pairwise rankin... ; || Jingtao Ding...

Improving Entity Recommendation with Search Log and Multi-Task Learning 2019-02-21 05:58:57

Entity recommendation, providing search users with an improved experience by assisting themin finding related entities for a given query, has become an indispensable feature of today’s Websearch engine. Existing studies typically only consider the query issued at the current time step while igno... || Entity recommendation; Baidu； BiLSTM; || Huang, Jizhou and Zhang, Wei and Sun, Yaming and Wang, Haifeng and Liu, Ting...

WWW 23

Austin, Texas, USA,

AAAI 23

Washington, DC, USA,

SIGIR2022

Madrid, Spain, July 11th to 15th, 2022

WSDM2023

TBD,

IJCAI2022

Austria, July 23-29, 2022