New cold-start recommendation method for recommender systems.
problem Making accurate recommendations for users and items with limited data.
method Proposes a faster cold-start prediction formula in collective matrix factorization models.
result Improved cold-start recommendations with reliable predictions for new users, better than non-personalized recommendations.
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. …
A hybrid approach uses RNNs to recommend news articles based on context and session history.
problem Challenging news recommendation due to varying user interests and factors.
method Context-aware, hybrid, deep learning approach using RNNs with additional information types.
result Significantly higher recommendation accuracy and catalog coverage compared to other session-based algorithms.
A graph traversal algorithm for cold-start news recommendation using named entities.
problem Cold-start news recommendation for articles without user-specific information.
method Graph traversal algorithm and novel weighting scheme for named entities over a knowledge graph.
result Our method produces stronger Pearson correlation to human similarity scores than other cold-start methods.
A deep learning architecture for news session-based recommendations.
problem Challenges in news recommendation systems, including sparse user profiling and dynamic user preferences.
method Hybrid approach combining text and metadata features, session-based recommendations with Recurrent Neural Networks, and temporal offline evaluation.
result Significant improvement in top-n accuracy and ranking metrics (10% Hit Rate and 13% MRR) over best benchmark methods.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
problem Recommender systems learn from user choices but can stall if users blindly follow recommendations.
method The recommender learns user knowledge by observing choices, abstaining from recommending a choice when multiple alternatives produce similar payoffs.
result Learning rate and social welfare improve when the recommender abstains from recommending certain choices.
CHAMELEON uses RNNs to recommend news sequences better than other methods.
problem Improving news recommendation accuracy and catalog coverage.
method Hybrid meta-architecture CHAMELEON with RNNs for sequence modeling and side information.
result Significantly higher recommendation accuracy and catalog coverage.
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent k…
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.
New algorithm uses negative user preferences to improve recommendation quality.
problem Insufficient utilization of negative user preferences in recommender systems.
method Explicitly models negative user preferences to recommend more positive items.
result Improves recommendation quality by increasing accuracy and reducing negative items.
A method for cold-start playlist recommendation using multitask learning.
problem Playlist recommendation for new users and songs.
method Flexible multitask learning method that minimizes a bipartite ranking loss and a classification loss.
result The method performs well in cold-start scenarios on real datasets.
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.
Proposes a graph neural network for personalized news recommendation.
problem Data sparsity in news recommendation systems.
method Heterogeneous graph model + Graph Neural Networks + LSTM attention mechanism.
result Significantly outperforms state-of-the-art methods on news recommendation datasets.
CHAMELEON tackles news recommendation using deep learning, addressing cold-start issues.
problem Personalizing user experiences in a dynamic news search space.
method Modular reference architecture with different neural building blocks, leveraging user and article context, and modeling temporal decay and concept drift.
result CHAMELEON outperforms traditional and state-of-the-art session-based recommendation algorithms in accuracy, item coverage, novelty, and reduced item cold-start problem.
New feature mapping approach improves recommendation accuracy and explainability.
problem Balancing recommendation accuracy and explainability using metadata.
method Maps uninterpretable features to interpretable aspect features, minimizing both prediction and interpretation losses.
result Strong performance in recommendation and explainability, eliminating metadata need.
Proposes a new model for diversified interactive recommendation.
problem Previous methods focus on optimizing recommendation accuracy, ignoring diversity.
method DC2B model using determinantal point process and Thompson sampling-based variational Bayesian inference. result Demonstrates effectiveness of the proposed method through extensive experiments.
Proposes CSRN for better news recommendation by integrating RNN and UserCF.
problem Lack of societal influence in news recommendation.
method Integrates RNN-based sequential recommendation and UserCF, using a directed co-reading network.
result Significantly outperforms state-of-the-art approaches in news recommendation.
New approaches estimate recommendation metrics using sampling.
problem Understanding and resolving the use of sampling for recommendation evaluation.
method MLE and ME principles for empirical rank distribution recovery.
result Advantages of new approaches for top-k metrics estimation.
The purpose if this master's thesis is to study and develop a new algorithmic framework for Collaborative Filtering to produce recommendations in the top-N recommendation problem. Thus, we propose Lanczos Latent Factor Recommender (LLFR); a novel "big data friendly" collaborative filtering algorithm for top-N recommend…
Paper introduces a new job recommendation method using candidate job selection progression.
problem Traditional job recommendation methods are either filter-based or feature-based, limiting serendipitous and cold-start recommendations.
method Uses machine learning to analyze candidate job selection progression and derive latent competencies.
result Achieved best click-through rate in a real-world job recommender system.
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
problem Improving recommendation systems for citations and subject labels.
method Multi-modal adversarial autoencoders with adversarial regularization, sparsity, and input modality analysis.
result Adversarial regularization consistently improves recommendation performance.
New method to rank metrics on non-shuffled traffic.
problem Position bias in ranking metrics due to item display order.
method Leverage stochasticity of recommendation policy to mitigate position bias.
result Improved ranking metrics without shuffling recommendations.
New algorithm reduces regret in fatigue-aware online recommendation.
problem Fatigue leads to user abandonment in online recommendation systems.
method Thompson Sampling for combinatorial bandits with fatigue model.
result Polynomial regret bound in number of items, outperforming naive approaches.
Interprets feature interactions in ad-click prediction models.
problem Improving interpretability of black-box recommender systems.
method Interprets feature interactions from a source model and encodes them in a target model.
result Interpretations significantly outperform existing recommender models.
Graph neural networks improve cold start for new items in recommender systems.
problem Cold start problem for new items in recommender systems.
method Item hierarchy graphs and bespoke graph neural network architecture.
result Our method achieves better forecasting quality than state-of-the-art with comparable computational time.
Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence. In this article, we propose an innovative method, namely the recommendation engine of multilayers (REM), for tenso…
Proposes auditing for envy-freeness in recommender systems to assess individual preferences.
problem Auditing fairness in recommender systems for individual preferences.
method Formulates a pure exploration problem in multi-armed bandits, proposing a sample-efficient algorithm with theoretical guarantees.
result Algorithm ensures fairness without deteriorating user experience on real-world datasets.
A fuzzy recommender system using PageRank for community detection.
problem Improving recommendation systems for large user communities.
method Fuzzy community detection using personalized PageRank.
result The method outperforms recent recommender systems on MovieLens and FilmTrust datasets.
Proposes a new model for explainable recommendation systems.
problem Developing predictive models that provide explanations for item recommendations.
method Generalized Additive Models with Manifest and Latent Interactions (GAMMLI).
result Advantages in both predictive performance and explainability.
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressi…
New method adapts to user preferences dynamically, improving recommendation models.
problem Current recommendation models lack dynamic adaptation to changing user preferences.
method Preference Discerning with LLM-Enhanced Generative Retrieval
result Mender achieves state-of-the-art performance in adapting to evolving user preferences.
A new method for better recommendation by accounting for hidden factors.
problem Hidden factors affecting both watched movies and ratings.
method Two-stage probabilistic models to remove bias due to confounding.
result Improved recommendation and stable performance against interventions.
Proposes a graph-based system for personalized news recommendation considering multiple user behaviors.
problem Lack of considering multiple user behaviors in news recommendation systems.
method Builds an interaction behavior graph, applies DeepWalk and G-CNN for news and behavior sequence representations, introduces core and coritivity features.
result Achieves personalized news recommendation considering user's concentration degree of interests.
Proposes a deep hybrid model for better recommendation systems.
problem Limited studies on hybrid recommender systems and the need for more advanced approaches.
method Integrates deep learning with ID embeddings and auxiliary features for improved recommendation.
result Improves recommendation results over deep learning models using ID embeddings.
The majority of recommender systems are designed to recommend items (such as movies and products) to users. We focus on the problem of recommending buyers to sellers which comes with new challenges: (1) constraints on the number of recommendations buyers are part of before they become overwhelmed, (2) constraints on th…
This study improves user segmentation for online news recommendation systems.
problem Challenges in building modern recommender systems due to dynamic environments and data sparsity.
method Trend-responsive unsupervised user segmentation using multi-armed bandits.
result Significant improvements in online A/B tests compared to global-optimization algorithms.
Proposes a deep learning model for timely and accurate recommendations.
problem Inability to provide timely recommendations and ranking issues with implicit feedback.
method Unified cross-network solution using listwise ranking for implicit data.
result Superior performance in accuracy, novelty, and diversity compared to baselines.
A new model for context-aware recommendations using LSTM and latent context.
problem Challenges in incorporating context into recommendation models, especially sparsity and dimensionality issues.
method Sequential latent context modeling using LSTM, reducing context dimensions to a compressed latent space.
result The proposed SLCM outperforms state-of-the-art CARS models in empirical analysis.
New model recommends stocks considering individual preferences and diversification.
problem Inaccurate stock price predictions and ignoring investment theories.
method Portfolio Temporal Graph Network Recommender (PfoTGNRec) incorporating diversification-enhancing sampling.
result PfoTGNRec outperforms state-of-the-art models in real-world data.
Memory-Augmented Meta-Optimization improves cold-start recommendation.
problem Cold-start problem in recommender systems for new users or items.
method Memory-Augmented Meta-Optimization approach with personalized and task-specific memories.
result Significant improvement in cold-start recommendation performance on multiple datasets.
Podcast recommendations improved by analyzing user listening paths.
problem Challenges in recommending podcasts effectively.
method Analyzes user listening paths as sequential trajectories for recommendations.
result 450% increase in effectiveness over baseline.
XploVAE improves recommendation by balancing known and novel items.
problem Balancing known and novel items for better recommendations.
method Constructs user-specific subgraphs for exploitation and exploration, learns personalized item embeddings.
result Demonstrates effectiveness on various real-world datasets.
ADER addresses continual learning in session-based recommendation by periodically replaying exemplars with adaptive distillation.
problem Catastrophic forgetting in continual learning of session-based recommenders.
method Periodically replaying previous training samples (exemplars) with an adaptive distillation loss.
result ADER consistently outperforms other continual learning techniques and even all historical data at every update cycle.
MKR uses knowledge graphs to enhance recommendation, improving performance across various categories.
problem Collaborative filtering's sparsity and cold start issues in real-world recommendation scenarios.
method MKR is a deep end-to-end framework that leverages knowledge graph embedding for recommendation, associating two tasks through cross&compress units.
result MKR achieves substantial gains in movie, book, music, and news recommendation, outperforming state-of-the-art baselines.
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.
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
A new KD model for collaborative filtering improves top-N recommendation performance.
problem Challenges in applying KD to recommender models due to feedback sparsity and ambiguity.
method Proposes a new KD model (CD) for collaborative filtering, reformulating a loss function, using probabilistic rank-aware sampling, and developing training strategies.
result Outperforms state-of-the-art methods by 2.7-33.2% in hit rate (HR) and 2.7-29.1% in NDCG.
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…