诺库维普

Hierarchical Gating Networks for Sequential Recommendation 2019-08-21 05:52:10

The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still... || 组织set（session）中的item embeddings; || Chen Ma...

DAML: Dual Attention Mutual Learning between Ratings andReviews for Item Recommendation 2019-08-20 11:57:07

Despite the great success of many matrix factorization based collaborative filtering approaches, there is still much space for improvement in recommender system field. One main obstacle is the cold-start and data sparseness problem, requiring better solutions. Recent studies have attempted to integr... ; || Bo Du...

Dual Sequential Prediction Models Linking SequentialRecommendation and Information Dissemination 2019-08-20 11:41:31

Sequential recommendation and information dissemination are two traditional problems for sequential information retrieval. The common goal of the two problems is to predict future user-item interactions based on past observed interactions. The difference is that the former deals with users’ historie... ; || Xiaofeng Gao...

Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation 2019-08-12 01:37:10

In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained seque... || Sequential recommendation, multi-hop reasoning, taxonomy, category; memory model； wsdm 19; || Jin Huang; Wayne Xin Zhao...

Self-Attentive Sequential Recommendation 2019-08-06 00:10:35

Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the ‘con- text’ of users’ activities on the basis of actions they have performed recently. To capture such patterns, two approaches have proliferated: Markov Chains (MCs) and Recurrent Neural Networks (RN... || ICDM；dropout；残差处理；点乘对称性；复杂度分析；内存分析（参数数量）；神经网络技巧（残差输入）; || Wang-Cheng Kang, Julian McAuley...

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine 2019-08-05 23:42:00

Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning-based model and attention mechanism in various tasks in computer vision (CV) and natural language processing (NLP). How to ... || attention; second-order; FFM; || JunlinZhang...

Leveraging app usage contexts for app recommendation: a neural approach 2019-07-30 05:02:55

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...

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