We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). ... || GRU4Rec-basic; || Bal´azs Hidasi...
Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of... || Session Recommendation; Streaming Recommendation; Attention Model; Matrix Factorization； GRU；paired sample t-test; 配对样本t检验; || Lei Guo...
A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often cons... || purpose; session-based; ijcai; || Liang hu...
Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the... || attention nn; ijcai；aggregation operation; || Zibin Zheng; Xiangnan He...
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...
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...
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...
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...
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...
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...
