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Active Learning for Recommender Systems
TLDR
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.Abstract:
Recommender systems learn user preferences and provide them personalized recommendations. Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new-user problem. A simple and effective way to overcome this problem is posing queries to new users so that they express their preferences about selected items, e.g., by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e., ratings from users that will help most in determining their interests. Active learning is a learning algorithm that is able to interactively query the Oracle to obtain labels for data instances. The Oracle is a user or teacher who knows the labels. The aim of this dissertation [8] 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. In the recommender system context, new users play the role of the Oracle and provide ratings (labels) to items (data instances). Specifically, the following questions are addressed in this dissertation: (1) which recommendation model is suitable for active-learning purposes? (Sect. 2) (2) how can active learning criteria be adapted and customized for the new-user problem and which one is the best? (Sect. 3) (3) what are the specific requirements and properties of the new-user problem that do not exist in active learning and how can new active learning methods be developed based on these properties? (Sects. 4, 5).read more
Citations
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Proceedings ArticleDOI
Towards Conversational Recommender Systems
TL;DR: 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.
Journal ArticleDOI
Content-Based Video Recommendation System Based on Stylistic Visual Features
Yashar Deldjoo,Mehdi Elahi,Paolo Cremonesi,Franca Garzotto,Pietro Piazzolla,Massimo Quadrana +5 more
TL;DR: A new content-based recommender system that encompasses a technique to automatically analyze video contents and to extract a set of representative stylistic features grounded on existing approaches of Applied Media Theory, to improve the accuracy of recommendations.
Proceedings ArticleDOI
Interactive collaborative filtering
TL;DR: This paper forms the interactive CF with the probabilistic matrix factorization (PMF) framework, and leverage several exploitation-exploration algorithms to select items, including the empirical Thompson sampling and upper confidence bound based algorithms.
Journal ArticleDOI
Interacting with Recommenders—Overview and Research Directions
Michael Jugovac,Dietmar Jannach +1 more
TL;DR: This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems, covering existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process.
Journal ArticleDOI
Alleviating the new user problem in collaborative filtering by exploiting personality information
TL;DR: In this paper, the authors proposed a new user problem in recommender systems based on the exploitation of user personality information, which directly improves the recommendation prediction model by incorporating user's personality information.
References
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Journal ArticleDOI
Active learning with statistical models
TL;DR: In this article, the optimal data selection techniques have been used with feed-forward neural networks and showed how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression.
Proceedings Article
Less is More: Active Learning with Support Vector Machines
Greg Schohn,David Cohn +1 more
TL;DR: A simple active learning heuristic is described which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks and frequently does so in less time than the naive approach of training on all available data.
Proceedings ArticleDOI
Functional matrix factorizations for cold-start recommendation
TL;DR: 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.
Proceedings ArticleDOI
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
TL;DR: The evaluation indicates that the proposed online-update methods are accurate in approximating a full retrain of a RKMF model while the runtime of online-updating is in the range of milliseconds even for huge datasets like Netflix.
Proceedings ArticleDOI
Adaptive bootstrapping of recommender systems using decision trees
TL;DR: An efficient tree learning algorithm is detailed, specifically tailored to the unique properties of the problem, and several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method.