Embedding models for recommendation under contextual constraints

06/21/2019
by   Syrine Krichene, et al.
0

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. The conventional approach, for both context-aware and standard models, is to retrieve items and apply the constraints as independent operations. The order in which these two steps are executed can induce significant problems. For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i.e., less popular items). As a result, the additional information that the constraint brings about user intent may not be accurately captured. In this paper we propose integrating the information provided by the contextual constraint into the similarity computation, by merging constraint application and retrieval into one operation in the embedding space. This technique allows us to generate high-quality recommendations for the specified constraint. Our approach learns constraints representations jointly with the user and item embeddings. We incorporate our methods into a matrix factorization model, and perform an experimental evaluation on one internal and two real-world datasets. Our results show significant improvements in predictive performance compared to context-aware and standard models.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/23/2021

CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation

Sequential recommendation techniques provide users with product recommen...
research
08/24/2018

A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations

Trip recommendation is an important location-based service that helps re...
research
03/18/2022

SiMCa: Sinkhorn Matrix Factorization with Capacity Constraints

For a very broad range of problems, recommendation algorithms have been ...
research
02/04/2020

Context-Aware Recommendations for Televisions Using Deep Embeddings with Relaxed N-Pairs Loss Objective

This paper studies context-aware recommendations in the television domai...
research
08/20/2022

HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations

The recent popularity of edge devices and Artificial Intelligent of Thin...
research
10/08/2021

Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

Capturing users' precise preferences is of great importance in various r...
research
07/07/2019

Search-Based Serving Architecture of Embeddings-Based Recommendations

Over the past 10 years, many recommendation techniques have been based o...

Please sign up or login with your details

Forgot password? Click here to reset