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Found 15 papers, 6 papers with code
Feature Learning based Deep Supervised Hashing with Pairwise Labels
For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning.
Column sampling based discrete supervised hashing
COSDISH is an iterative method, in each iteration of which several columns are sampled from the semantic similarity matrix and then the hashing code is decomposed into two parts which can be alternately optimized in a discrete way.
Translation-based Recommendation
Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems.
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i. e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features.
Self-Attentive Sequential Recommendation
Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently.
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Recommendation Through Mixtures of Heterogeneous Item Relationships
Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data.
Complete the Look: Scene-based Complementary Product Recommendation
We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
Sequential patterns play an important role in building modern recommender systems.
Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation
Generating the Top-N recommendations from a large corpus is computationally expensive to perform at scale.
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
In this paper, we seek to learn highly compact embeddings for large-vocab sparse features in recommender systems (recsys).
Learning to Embed Categorical Features without Embedding Tables for Recommendation
Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings.
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.
Farzi Data: Autoregressive Data Distillation
We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure.
