诺库维普

Learning Hierarchical Representation Model for Next Basket Recommendation 2019-05-17 03:22:59

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...

Local Latent Space Models for Top-N Recommendation 2019-05-02 03:23:44

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...

Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model 2019-05-01 23:29:41

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...

Buy It Again: Modeling Repeat Purchase Recommendations 2019-05-01 23:16:43

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...

Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation 2019-04-29 10:24:51

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...

Session-based Recommendation with Graph Neural Networks 2019-04-29 10:21:20

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; ...

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation 2019-04-29 10:19:08

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...

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