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.
( Image credit: CuMF_SGD )
Benchmarks
These leaderboards are used to track progress in Recommendation Systems
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Libraries
Use these libraries to find Recommendation Systems models and implementations
MovieLens
Netflix Prize
Gowalla
MIND
Yelp2018
ReDial
Polyvore
Subtasks
Latest papers
Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models
With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift.
Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
ghdtjr/a-llmrec
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Despite their effectiveness under cold scenarios, we observe that they underperform simple traditional collaborative filtering models under warm scenarios due to the lack of collaborative knowledge.
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items.
Cluster-based Graph Collaborative Filtering
This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them.
HELLINGER-UCB: A novel algorithm for stochastic multi-armed bandit problem and cold start problem in recommender system
In this paper, we study the stochastic multi-armed bandit problem, where the reward is driven by an unknown random variable.
NFARec: A Negative Feedback-Aware Recommender Model
In this paper, we propose a negative feedback-aware recommender model (NFARec) that maximizes the leverage of negative feedback.
End-to-end training of Multimodal Model and ranking Model
In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption.
A Comprehensive Survey on Self-Supervised Learning for Recommendation
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences.
Does Knowledge Graph Really Matter for Recommender Systems?
We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users.
Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open Challenges
The emergence of Large Language Models (LLMs) has achieved tremendous success in the field of Natural Language Processing owing to diverse training paradigms that empower LLMs to effectively capture intricate linguistic patterns and semantic representations.
