• Corpus ID: 54434669

Active Learning in Recommendation Systems with Multi-level User Preferences

  title={Active Learning in Recommendation Systems with Multi-level User Preferences},
  author={Yuheng Bu and Kevin Small},
While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences. In such settings, considering queries at different levels of granularity to optimize user information acquisition is crucial to efficiently providing a good user experience. In this work, we study the active learning problem with multi-level user preferences within the collective matrix factorization (CMF) framework. CMF… 

Figures from this paper

Toward Building Conversational Recommender Systems: A Contextual Bandit Approach

This work proposes conversational recommendation in which the system occasionally asks questions to the user about her interest, and proposes a new UCB- based algorithm, and theoretically proves that the new algorithm can indeed reduce the amount of exploration in learning.

Conversational Contextual Bandit: Algorithm and Application

The Conversational UCB algorithm (ConUCB) is designed to address two challenges in conversational contextual bandit: which key-terms to select to conduct conversation, and how to leverage conversational feedbacks to accelerate the speed of bandit learning.



Active Learning for Recommender Systems

  • R. Karimi
  • Computer Science
    KI - Künstliche Intelligenz
  • 2014
The aim of this dissertation is to take inspiration from the literature of active learning for classification (regression) problems and develop new methods for the new-user problem in recommender systems.

Functional matrix factorizations for cold-start recommendation

Functional matrix factorization is presented, a novel cold-start recommendation method that solves the problem of initial interview construction within the context of learning user and item profiles and associate latent profiles for each node of the tree, which allows the profiles to be gradually refined through the interview process based on user responses.

Learning preferences of new users in recommender systems: an information theoretic approach

The work of [23] is extended by incrementally developing a set of information theoretic strategies for the new user problem by proposing an offline simulation framework and evaluating the strategies through extensive offline simulations and an online experiment with real users of a live recommender system.

Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

This work formalizes this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of the model.

Towards Conversational Recommender Systems

This paper develops a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences, and finds that this framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions.

Learning multiple-question decision trees for cold-start recommendation

A novel algorithm that learns to conduct the interview process guided by a decision tree with multiple questions at each split is proposed, which outperforms state-of-the-art approaches in terms of both the prediction accuracy and user cognitive efforts.

A Latent Source Model for Online Collaborative Filtering

A model for online recommendation systems is introduced, cast item recommendation under the model as a learning problem, and the performance of a cosine-similarity collaborative filtering method is analyzed.

Collective Factorization for Relational Data : An Evaluation on the Yelp Datasets

This paper presents a generic approach to factorization of relational data that collectively models all the relations in the database, and demonstrates effective utilization of additional information for held-out rating and attribute prediction on four Yelp datasets.

Relational learning via collective matrix factorization

This model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems, which can handle any pairwise relational schema and a wide variety of error models.