Recommendation Systems
1442 papers with code • 54 benchmarks • 54 datasets
The Recommendation Systems task is to produce a list of recommendations for a user. The most common methods used in recommender systems are factor models (Koren et al., 2009; Weimer et al., 2007; Hidasi & Tikk, 2012) and neighborhood methods (Sarwar et al., 2001; Koren, 2008). Factor models work by decomposing the sparse user-item interactions matrix to a set of d dimensional vectors one for each item and user in the dataset. Factor models are hard to apply in session-based recommendations due to the absence of a user profile. On the other hand, neighborhood methods, which rely on computing similarities between items (or users) are based on co-occurrences of items in sessions (or user profiles). Neighborhood methods have been used extensively in session-based recommendations.
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Libraries
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Latest papers with no code
Relationship Discovery for Drug Recommendation
Medication recommendation systems are designed to deliver personalized drug suggestions that are closely aligned with individual patient needs.
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Our comprehensive theoretical and empirical investigations lead to two core insights: 1) Item popularity is memorized in the principal singular vector of the score matrix predicted by the recommendation model; 2) The dimension collapse phenomenon amplifies the impact of principal singular vector on model predictions, intensifying the popularity bias.
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors.
SIGformer: Sign-aware Graph Transformer for Recommendation
Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences.
Automated Similarity Metric Generation for Recommendation
Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions.
Characterizing and modeling harms from interactions with design patterns in AI interfaces
The proliferation of applications using artificial intelligence (AI) systems has led to a growing number of users interacting with these systems through sophisticated interfaces.
Deep Pattern Network for Click-Through Rate Prediction
These patterns harbor substantial potential to significantly enhance CTR prediction performance.
Recommender Systems in Financial Trading: Using machine-based conviction analysis in an explainable AI investment framework
Analysts' conviction around their recommendations and their "paper trading" track record are two crucial workflow components between analysts and portfolio construction.
Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems
To fill this gap, we propose Behavior Alignment, a new evaluation metric to measure how well the recommendation strategies made by a LLM-based CRS are consistent with human recommenders'.
Course Recommender Systems Need to Consider the Job Market
In light of the job market's rapid changes and the current state of research in course recommender systems, we outline essential properties for course recommender systems to address these demands effectively, including explainable, sequential, unsupervised, and aligned with the job market and user's goals.