DeepAI AI Chat
Log In Sign Up

Latent Unexpected Recommendations

07/27/2020
by   Pan Li, et al.
NYU college
0

Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expectations by modeling unexpectedness in the feature space, thus resulting in the loss of accuracy measures in order to improve unexpectedness performance. Contrast to these prior models, we propose to model unexpectedness in the latent space of user and item embeddings, which allows to capture hidden and complex relations between new recommendations and historic purchases. In addition, we develop a novel Latent Closure (LC) method to construct hybrid utility function and provide unexpected recommendations based on the proposed model. Extensive experiments on three real-world datasets illustrate superiority of our proposed approach over the state-of-the-art unexpected recommendation models, which leads to significant increase in unexpectedness measure without sacrificing any accuracy metric under all experimental settings in this paper.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/04/2019

Latent Unexpected and Useful Recommendation

Providing unexpected recommendations is an important task for recommende...
04/05/2016

Comparative Deep Learning of Hybrid Representations for Image Recommendations

In many image-related tasks, learning expressive and discriminative repr...
06/05/2021

PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

Classical recommender system methods typically face the filter bubble pr...
11/15/2017

BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations

Recommenders have become widely popular in recent years because of their...
02/07/2018

CryptoRec: Secure Recommendations as a Service

Recommender systems rely on large datasets of historical data and entail...
09/18/2021

Inductive Conformal Recommender System

Traditional recommendation algorithms develop techniques that can help p...
02/02/2019

RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings

The rapid growth of users' involvement in Location-Based Social Networks...