Recommendation Systems
1438 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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Latest papers with no code
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.
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.
Bullion: A Column Store for Machine Learning
The past two decades have witnessed columnar storage revolutionizing data warehousing and analytics.
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection.
Measuring the Predictability of Recommender Systems using Structural Complexity Metrics
This study introduces data-driven metrics to measure the predictability of RS based on the structural complexity of the user-item rating matrix.
Can Large Language Models Assess Serendipity in Recommender Systems?
In this investigation, a binary classification task was given to the LLMs to predict whether a user would find the recommended item serendipitously.
Enhancing Adaptive Video Streaming through Fuzzy Logic-Based Content Recommendation Systems: A Comprehensive Review and Future Directions
This review paper explores the integration of fuzzy logic into content recommendation systems for adaptive video streaming.
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform
This paper advocates for the broader adoption of FFT in vector database management, marking a significant stride towards addressing the challenges of data volume and complexity in AI research and applications.