Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-booste…
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48 results for “content recommendation”
Study shows online learning algorithms incentivize low-quality content, proposing new algorithms to improve quality.
problem Online learning algorithms in content recommender systems incentivize producers to create low-quality content.
method Analyzed the game between producers and content quality, designed new learning algorithms to incentivize high effort and quality.
result New algorithms incentivize producers to invest high effort and achieve high user welfare, improving content quality.
A new model considers fatigue in online content recommendation systems.
problem Fatigue in users due to overexposure and boredom from similar recommendations.
method Proposed a fatigue-aware Dependent Click Model (DCM) and two learning algorithms.
result Developed algorithms with regret bounds for learning content relevance and fatigue effects.
Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations…
Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start settingcs.IR
Paper presents content-based models for game recommendation in cold start scenarios.
problem Cold start problem in game recommendation where new games and players have no historical data.
method Uses survey data to develop content-based interaction models that generalize to new games, players, and both.
result Content models outperform collaborative filtering in predicting new interactions.
We quantify content availability and user discovery opportunities in recommender systems.
problem Determining the maximum probability of recommending content to users.
method Stochastic reachability to compute upper bounds on recommendation likelihood.
result Reachability metrics can detect biases and diagnose user discovery limitations.
Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal…
Proposes a model to optimize feedback for content creators on social media.
problem Nurturing content creation on social media platforms.
method Modeling feedback distribution to incentivize creators and optimize newsfeed experience.
result Optimized newsfeed for content creators without compromising user experience.
New study shows personalized content recommendations can lead to polarization of user preferences.
problem Personalized content recommendations can alter user preferences, leading to polarization.
method Used a model of preference dynamics to explore how personalized content affects user preferences.
result Standard reward maximization algorithms achieve only constant regret in personalized recommendation environments.
The study aims to prevent unfair content presentation in recommender systems.
problem Over- and under-presentation of content leads to biased user preference estimates.
method Two models are considered: one that ignores systematic and limited exposure, and another that conditions on limited exposure.
result Ignoring systematic presentations overestimates promoted options and underestimates censored alternatives.
Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we expl…
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents un…
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. D…
Spectral banditsstat.ML
Bandit problem on graphs aims to recommend items with high expected ratings.
problem Online learning problems involving graphs, such as content-based recommendation.
method Study of a bandit problem on graphs, introducing effective dimension and proposing algorithms.
result Proposed algorithms scale linearly and sublinearly in the effective dimension, improving cumulative regret.
With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where us…
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, w…
In recent years, content recommendation systems in large websites (or \emph{content providers}) capture an increased focus. While the type of content varies, e.g.\ movies, articles, music, advertisements, etc., the high level problem remains the same. Based on knowledge obtained so far on the user, recommend the most d…
The paper tackles a bandit problem on graphs with smooth functions, aiming to recommend items with high expected ratings.
problem Online learning problems involving graphs, such as content-based recommendation.
method Introduced the notion of effective dimension and proposed two algorithms for solving the problem.
result The algorithms can learn good estimators of user preferences from just tens of nodes evaluations.
We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification cost…
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an exp…
Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Matthew effect" becomes increasingly evident. While the head contents get more and more popular, many competitive long-tail contents are diffi…
Study bandit problem on smooth graph functions for recommender systems.
problem Online learning problems involving graphs, like content-based recommendation.
method Introduced spectral bandit problem and two algorithms that scale linearly in effective dimension.
result Learned user preferences for thousands of items from just tens nodes evaluations.
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet applications propose to predict a new item's interaction from the possessing contents. However, it is…
Email has remained a principal form of communication among people, both in enterprise and social settings. With a deluge of emails crowding our mailboxes daily, there is a dire need of smart email systems that can recover important emails and make personalized recommendations. In this work, we study the problem of pred…
Recommending appropriate items to users is crucial in many e-commerce platforms that contain implicit data as users' browsing, purchasing and streaming history. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender sy…
New algorithm poLinUCB improves online learning in content recommendation platforms.
problem Improving efficiency in content recommendation platforms with post-serving context.
method Novel contextual bandit problem with post-serving contexts and a new algorithm, poLinUCB.
result Achieves tight regret under standard assumptions and significant benefit of utilizing post-serving contexts.
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
New term ADS describes how machine learning can change user behavior.
problem Machine learning systems can unintentionally change user behavior, affecting performance.
method Introduced `unit tests` and mitigation strategy for hidden incentives in auto-induced distributional shift.
result Meta-learning and Q-learning sometimes fail unit tests but pass with mitigation strategy.
Spotify improves content mix using contextual bandits.
problem Skewed historical data and varying user preferences across contexts.
method Contextual bandits to dynamically learn optimal content type distribution.
result Improved precision and user engagement with under-represented content types.
Optimizes long-term social welfare in recommender systems by matching users to providers.
problem Realistic recommender systems dynamics affect all agents, not just users.
method Formulated as an optimal constrained matching problem, solved using dynamical system equilibrium selection.
result Ensures maximal social welfare with diverse viable providers, improving over myopic matching.
Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid.…
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring re…
The profusion of online news articles makes it difficult to find interesting articles, a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers. However, news recommendation is challenging because the most relevant articles are often new content seen by few users. …
App enhances midwives' skills in low-income countries.
problem Low-income countries have high maternal and child mortality rates.
method Deep learning models predict midwives' interest in learning materials.
result Deep learning models accurately predict midwives' interest in learning materials.
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based …
With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods …
New approach to disentangle utility from impulse in recommendation systems.
problem Difficulty in inferring user utility from engagement signals.
method Generative model based on self-exciting Hawkes process to infer utility from return probability.
result It is possible to disentangle System-1 and System-2 decision processes to optimize content based on user utility.
New algorithm optimizes long-term user satisfaction in recommendation systems.
problem Optimizing long-term user satisfaction in recommendation systems with delayed rewards.
method Developed a predictive model of delayed rewards and a bandit algorithm that balances exploration and exploitation.
result Our approach results in substantially better performance compared to short-term or delayed optimization.
We propose Style Conditioned Recommendations (SCR) and introduce style injection as a method to diversify recommendations. We use Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile learned from item content data. This allows us to apply style tr…
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap bet…
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using …
Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation b…
Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content …
New algorithm optimizes for long-term user satisfaction in delayed reward settings.
problem Optimizing for long-term user satisfaction in delayed reward settings.
method Developed a predictive model of delayed rewards and a bandit algorithm that combines rewards and surrogate outcomes.
result Our algorithm significantly outperforms methods that optimize for short-term proxies or rely solely on delayed rewards.
Deep learning improves conversational recommender systems.
problem Creating effective goal-oriented dialogue systems through natural language interactions.
method Apply deep learning techniques to conversational recommender systems.
result Deep learning models enhance user preference learning in CRS.
New method for evaluating sequential recommendations with lower variance.
problem Evaluating good sequences of music, video, news, and e-commerce recommendations.
method Proposes a new counterfactual estimator for sequential reward interactions with lower variance and asymptotic unbiasedness.
result Our method outperforms existing methods in bias and data efficiency for sequential track recommendations.
Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the other hand, is attractive because textual content is often very informative. In this paper we describe …
Cross-domain recommendation has long been one of the major topics in recommender systems. Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxilliary contents, e.g., images and review texts, and th…