A Unified Model for Recommendation with Selective Neighborhood Modeling

10/19/2020
by   Jingwei Ma, et al.
0

Neighborhood-based recommenders are a major class of Collaborative Filtering (CF) models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on specific subsets of users for recommendation. However, the neighborhood information is not necessarily always informative, and the noises in the neighborhood can negatively affect the model performance. To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations. The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa. In addition, a user-neighbor component is proposed to explicitly regularize user-neighbor proximity in the latent space. These two components are combined into a unified model to complement each other for the recommendation task. Extensive experiments on three publicly available datasets show that the proposed model consistently outperforms state-of-the-art neighborhood-based recommenders. We also study different variants of the proposed model to justify the underlying intuition of the proposed hybrid gated network and user-neighbor modeling components.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 3

page 11

page 12

page 16

page 18

page 19

page 21

10/23/2020

NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation

Learning informative representations (aka. embeddings) of users and item...
02/13/2022

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

Recently, graph collaborative filtering methods have been proposed as an...
04/29/2018

Collaborative Memory Network for Recommendation Systems

Recommendation systems play a vital role to keep users engaged with pers...
08/21/2018

CoBaR: Confidence-Based Recommender

Neighborhood-based collaborative filtering algorithms usually adopt a fi...
04/30/2020

A more secure IPv6 neighborhood process

The process of neighborhood establishment in an IPv6 network is made out...
12/07/2018

Gated Attentive-Autoencoder for Content-Aware Recommendation

The rapid growth of Internet services and mobile devices provides an exc...
08/20/2018

Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations

In this paper, we present work-in-progress on applying user pre-filterin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.