A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New methods improve recommendation accuracy for users and items with few ratings.
TransCF improves recommendation by modeling user-item relationships with translation vectors.
IGMC learns inductive matrix completion without side info.
Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do n…
SAIN integrates user-item feedback with content attributes for better recommendation.
GEMRank embeds users and items using co-occurrence relations for better collaborative filtering.
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems. In recent times, different variants of deep learning algorithms have been explored in this setting to improve the task of making a personalized recommendation with user-item interaction data. The idea …
We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchas…
A new method speeds up ALS for recommender systems by subsampling key elements.
A method learns matrix factorization from diverse matrices and applies the knowledge to unseen matrices.
Improved item recommendations for repeat interactions using sequence analysis.
Low rank matrix factorisation is often used in recommender systems as a way of extracting latent features. When dealing with large and sparse datasets, traditional recommendation algorithms face the problem of acquiring large, unrestrained, fluctuating values over predictions especially for users/items with very few co…
CSA improves recommender systems by learning context-aware feature representations.
This paper introduces new methods to improve 1-bit matrix completion by considering cluster effects.
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…
Generates massive synthetic data sets for recommender systems.
DBRec discovers latent groups to improve recommendation.
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we prop…
The goal of a recommendation system is to predict the interest of a user in a given item by exploiting the existing set of ratings as well as certain user/item features. A standard approach to modeling this problem is Inductive Matrix Completion where the predicted rating is modeled as an inner product of the user and …
Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. T…
Proposes MCCF to distinguish latent purchasing motivations in user-item interactions.
CGM combines SSL and LFM for better recommendation performance.
The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.
Predict artist efficiency in VFX shots using matrix completion.
Matrix factorization is a simple and effective solution to the recommendation problem. It has been extensively employed in the industry and has attracted much attention from the academia. However, it is unclear what the low-dimensional matrices represent. We show that matrix factorization can actually be seen as simult…
Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, mod…
Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an ex…
New neural network predicts user-item relationships in evolving graphs.
We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we formulate the GP in a way that allows the incorporation of low-rank matrix factorisation, arriving at o…
Secure social recommendation framework using secret sharing.
Integrates contextual constraints into embedding models for better recommendation quality.
Matrix factorization (MF) has become a common approach to collaborative filtering, due to ease of implementation and scalability to large data sets. Two existing drawbacks of the basic model is that it does not incorporate side information on either users or items, and assumes a common variance for all users. We extend…
Data poisoning attacks can manipulate recommender systems to recommend target items.
New method improves stability of collaborative filtering.
Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferen…
NeuCDCF combines matrix factorization and deep learning for cross-domain recommendation.
New model improves recommendation systems by analyzing user-item interactions.
Expands small recommendation datasets to industrial scale.
OMIC improves matrix completion with orthonormal side information and nuclear-norm regularization.
PGRec improves recommendation by modeling user-item preferences as a graph and embedding it for better predictions.
Latent factor models for Recommender Systems with implicit feedback typically treat unobserved user-item interactions (i.e. missing information) as negative feedback. This is frequently done either through negative sampling (point--wise loss) or with a ranking loss function (pair-- or list--wise estimation). Since a ze…
A new linear GCN model improves recommendation performance for large graphs.
We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings, protein-drug bindings, or ternary user-item-tag interactions. The canonical representation of such interactions is a matrix (or a higher-dimensional tensor) with an exchangeability property: the encoding's meaning…
CMTRF improves recommendation accuracy by transforming rating scales.
Linear models like EASE and SLIM are competitive in recommendation, and this work explores their theoretical relationship.
Improved neural model for social recommendation by integrating social and interest networks.
Recommendation is the task of improving customer experience through personalized recommendation based on users' past feedback. In this paper, we investigate the most common scenario: the user-item (U-I) matrix of implicit feedback. Even though many recommendation approaches are designed based on implicit feedback, they…
DMF improves POI recommendation privacy and efficiency.