诺库维普

Towards Better Representation Learning for Personalized News Recommendations: A Multi-Channel Deep Fusion Approach 2019-04-06 04:17:33

Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs). With the remarkable success of deep learn- ing tec... || cross-domain; NN; attention model; attentive model；NSVD; 用点击的item embedding 去表达user representation; || Jianxun Lian...

DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation 2019-04-03 09:27:27

Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embedding... || NCF; NSVD; 用item embedding表达user embedding, 反之亦可; 传统的user embedding + 用item embedding表达的user embedding;; || Weiyu Cheng...

Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback 2019-04-03 07:58:08

Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not va... || time; temporal; implicit feedback; Gaussian process; || Yong Liu...

PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training 2019-04-02 22:01:38

Recommender systems provide users with ranked lists of items based on individual’s preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term mod- els represent the interactions between users and items th... || Gan; Generative Adversarial Network; reinforcement learning; LSTM; MF; || Wei Zhao...

Interpretable Recommendation via Attraction Modeling: Learning Multilevel Attractiveness over Multimodal Movie Contents 2019-03-13 02:55:29

New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender syst... || attention; 绘图; || Liang Hu...

Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation 2019-03-05 23:24:13

Point-of-Interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing ... || physical distance; 一个点两个向量；非对称；asymmetric；时间或者地理距离信号影响; || Hao Wang...

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...

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