• Corpus ID: 203414550

Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

@inproceedings{Doan2019ReviewBasedCC,
  title={Review-Based Cross-Domain Collaborative Filtering: A Neural Framework},
  author={Thanh-Nam Doan and Shaghayegh Sherry Sahebi},
  booktitle={ComplexRec@RecSys},
  year={2019}
}
Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current crossdomain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain… 

Figures and Tables from this paper

Third workshop on recommendation in complex scenarios (ComplexRec 2019)

TLDR
The goal of the ComplexRec 2019 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

Overview of the Workshop on Recommendation in Complex Scenarios 2019 (ComplexRec 2019)

TLDR
The goal of the ComplexRec 2019 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

References

SHOWING 1-10 OF 23 REFERENCES

Cross-Domain Collaborative Filtering with Review Text

TLDR
This paper extends previous transfer learning models in collaborative filtering, from linear mapping functions to non-linear ones, and proposes a cross-domain recommendation framework with the review text incorporated.

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

TLDR
This work proposes a content-based recommendation system to address both the recommendation quality and the system scalability, and proposes to use a rich feature set to represent users, according to their web browsing history and search queries, using a Deep Learning approach.

It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering

TLDR
This paper proposes to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggests a cross-domain collaborative filtering based on CCA (CD-CCA), that proves to be successful in using the shared information between domains in the target recommendations.

Collaborative Deep Learning for Recommender Systems

TLDR
A hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix is proposed, which can significantly advance the state of the art.

Content-Based Cross-Domain Recommendations Using Segmented Models

TLDR
This work provides a generic framework for content-based cross-domain recommendations that can be used with var- ious classifiers and proposes an ecient method of feature augmentation to implement adaptation of domains.

Estimating Reactions and Recommending Products with Generative Models of Reviews

TLDR
This paper focuses on generating reviews as the model’s output, and shows that this can model can be used to generate plausible reviews and estimate nuanced reactions; provide personalized rankings of existing reviews; and recommend existing products more effectively.

Cross-domain recommender systems : A survey of the State of the Art

TLDR
This paper provides a formal statement of the problem, a review of the state of the art, and establishes a general taxonomy that let us to better characterize, categorize and compare the revised work.

Deep Learning Based Recommender System

TLDR
A taxonomy of deep learning-based recommendation models is provided and a comprehensive summary of the state of the art is provided, along with new perspectives pertaining to this new and exciting development of the field.

Transfer Learning in Collaborative Filtering for Sparsity Reduction

TLDR
This paper discovers the principle coordinates of both users and items in the auxiliary data matrices, and transfers them to the target domain in order to reduce the effect of data sparsity.

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering

TLDR
This paper builds novel models for the One-Class Collaborative Filtering setting, where the goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback and combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community.