A Collaborative Learning Framework to Tag Refinement for Points of Interest 2019-11-22 21:17:09
Tags of a Point of Interest (POI) can facilitate location-based services from many aspects like location search and place recommendation. However, many POI tags are often incomplete or imprecise, which may lead to performance degradation of tag-dependent applications. In this paper, we study the POI... || Tag refinement ; || Jingbo Zhou; Hui Xiong...

Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability 2019-11-22 15:27:14
Recent years have witnessed growing interests in developing deep models for incremental learning. However, existing approaches often utilize the fixed structure and online backpropagation for deep model optimization, which is difficult to be implemented for incremental data scenarios. Indeed, for st... || 增量学习; 在线学习; fisher information matrix;对之前学好的模型参数建模,从而保存之前的信息; || Yang Yang; Hui Xiong...

A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction 2019-11-21 09:32:58
The understanding of job mobility can benefit talent management operations in a number of ways, such as talent recruitment, talent development, and talent retention. While there is extensive literature showing the predictability of the organization-level job mobility patterns (e.g., in terms of the ... ; || Qingxin Meng; Hui Xiong...

Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling 2019-11-15 21:11:53
Sequential recommendation task aims to predict user preference over items in the future given user historical behaviors. The order of user behaviors implies that there are resourceful sequential patterns embedded in the behavior history which reveal the underlying dynamics of user interests. Various... || Sequential Recommendation, Collaborative Filtering, Co-Attention; GRU; || Jiarui Qin; Kan Ren...

WhyWe Go Where We Go: Profiling User Decisions on Choosing POIs 2019-11-14 10:44:06
While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, wh... || scalar projection; 一个利用scalar projection做的attention方法; || Renjun Hu; Xinjiang Lu...

Sequential Scenario-Specific Meta Learner for Online Recommendation 2019-11-07 20:13:24
Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems using few-shot learning and meta learning. Our... || few-shot learning; meta learning; LSTM; early-stop policy; learning rate;; || Zhengxiao Du...

Session-based recommendations with recurrent neural networks 2019-11-05 20:02:13
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...

Streaming Session-based Recommendation 2019-11-05 09:23:30
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...

Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks 2019-10-14 14:07:13
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...

Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network 2019-09-27 16:51:31
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...