诺库维普

Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation 2019-04-10 11:13:28

Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, pur- chases). Given a sequence of a particular type (e.g., purchases)– referred to as the target sequence, we seek to predict the next item expected to a... || LSTM; Cross domain; next-item; Siamese networks; || Duc-Trong Le, Hady W. Lauw and Yuan Fang...

Phrase Table as Recommendation Memory for Neural Machine Translation 2019-04-10 06:29:13

Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent but unfaithful translations. In this paper, we propose a method to alleviate this problem by using a phrase table as reco... || stacked LSTM; 规则结合ML；; || Yang Zhao...

Discrete Factorization Machines for Fast Feature-based Recommendation 2019-04-10 04:10:39

User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 107, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the comp... || FM; discrete; content-based; || Han Liu; Xiangnan He...

CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering 2019-04-09 00:26:53

Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as independent while ignoring the rich couplings within ... || non-IID; content based; CF based; CNN; NCF; || Quangui Zhang; Longbing Cao...

Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them 2019-04-08 09:23:29

A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with ... || interpretation; A-I model; aspect-item model；可解释性；memory based; memory-based；公式很漂亮; || Antonio Rago, Oana Cocarascu, Francesca Toni...

Recommendation with Multi-Source Heterogeneous Information 2019-04-08 03:18:14

Network embedding has been recently used in social network recommendations by embedding low- dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item informati... || point-wise recommendation; 多信息融合； random walk; || Li Gao...

LSTM Networks for Online Cross-Network Recommendations 2019-04-07 23:07:02

Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing... || Cross-domain; LSTM: attention model; next-item recommendation; || Dilruk Perera and Roger Zimmermann...

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