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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
