The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data re... ; || Yanan Xu...
Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, n... || Attention Network; 不同用户关注到的图片都parts可能会不一样；有些只关心部分，有些就会关心整体； 部分和整体; || Xu Chen; Yongfeng Zhang...
Next basket recommendation is a crucial task in market basket analysis. Given a user’s purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able ... || HRM; aggregation operation; next basket; || Pengfei Wang...
Users’ behaviors are driven by their preferences across various aspects of items they are potentially interested in purchasing, view- ing, etc. Latent space approaches model these aspects in the form of latent factors. Although such approaches have been shown to lead to good results, the aspects tha... ; || George Karypis...
Heterogeneous information network (HIN) has been widely adopted in recommender systems due to its excellence in modeling complex context information. Although existing HIN based recommendation methods have achieved performance improvement to some extent, they have two major shortcomings. First, thes... || Attention Mechanism； HIN; Graph; || Binbin Hu, Chuan Shi...
Repeat purchasing, i.e., a customer purchasing the same product multiple times, is a common phenomenon in retail. As more customers start purchasing consumable products (e.g., toothpastes, diapers, etc.) online, this phenomenon has also become prevalent in e-commerce. However, in January 2014, when ... || KDD; repeat recommendation; amazon; || Rahul Bhagat...
Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely consider the spatiotemporal interva... || time interval; distance interval; LSTM; Time-LSTM；减少LSTM参数的方法；CA; SIN; Gowalla; Brightkite; || Pengpeng Zhao; Yanchi Liu...
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obt... || 融合一个关系已知的矩阵；Graph Neural Networks (GNN); attention model; long- and short-term; session-based; Yoochoose; Diginetica; || Shu Wu; ...
Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), where the same item is r... || 所谓的repeat，指的是推荐可重复消费的物品；GRU；session encoder; attention model; OOCHOOSE; DIGINETICA; LASTFM; || Pengjie Ren...
With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, ... || CNN; RNN; Attention Model; 不一样的objective function； content-based; dynamic; 图画的不错；Adressa-1week; Adressa-10week; || Qiannan Zhu;...
