Modelling Users with Item Metadata for Explainable and Interactive Recommendation

  title={Modelling Users with Item Metadata for Explainable and Interactive Recommendation},
  author={Joey De Pauw and Koen Ruymbeek and Bart Goethals},
Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as “connecting relevant content to interested users”. Personalized recommendation algorithms achieve this goal by first building a profile of the user, either implicitly or explicitly, and then matching items with this profile to find relevant content. The more interpretable the profile and this “matching function” are, the easier it is to provide users with accurate and… 

Figures and Tables from this paper

Who do you think I am? Interactive User Modelling with Item Metadata

This work proposes a linear collaborative filtering recommendation model that builds user profiles within the domain of item metadata and demonstrates the interactive aspect of this model in an online application for discovering cultural events in Belgium.



Collaborative Filtering for Implicit Feedback Datasets

This work identifies unique properties of implicit feedback datasets and proposes treating the data as indication of positive and negative preference associated with vastly varying confidence levels, which leads to a factor model which is especially tailored for implicit feedback recommenders.

A generalized taxonomy of explanations styles for traditional and social recommender systems

The results of three different user studies are summarized, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.

Explaining collaborative filtering recommendations

This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.

A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems

This paper takes a ranking optimization approach that seeks to optimize embedding weights based on observed rank violations from earlier critiquing iterations and evaluates the proposed framework on two recommendation datasets containing user reviews.

Latent Linear Critiquing for Conversational Recommender Systems

This paper builds on an existing state-of-the-art linear embedding recommendation algorithm to align review-based keyphrase attributes with user preference embeddings and exploits the linear structure of the embedDings and recommendation prediction to formulate a linear program (LP) based optimization problem to determine optimal weights for incorporating critique feedback.

Matrix co-factorization for recommendation with rich side information and implicit feedback

This paper proposes matrix factorization techniques to incorporate rich user and item information into recommendation with implicit feedback in online scientific communities which exhibit two characteristics: there exists very rich information about users and items and the users in the scientific communities tend not to give explicit ratings to the resources.

Hybrid Recommender Systems: Survey and Experiments

  • R. Burke
  • Computer Science
    User Modeling and User-Adapted Interaction
  • 2004
This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.

Fast Multi-Step Critiquing for VAE-based Recommender Systems

M&Ms-VAE, a novel variational autoencoder for recommendation and explanation that is based on multimodal modeling assumptions, is presented, which is the first to dominate or match the performance in terms of recommendation, explanation, and multi-step critiquing.

Recommender systems: from algorithms to user experience

It is argued that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and additional measures that have proven effective are suggested.