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Found 21 papers, 11 papers with code
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling
However, the cross-item relations have been under-explored in the current multimodal pre-train models.
FashionReGen: LLM-Empowered Fashion Report Generation
Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports.
Contrastive Pre-training for Deep Session Data Understanding
In this work, we delve into deep session data understanding via scrutinizing the various clues inside the rich information in user sessions.
DiFashion: Towards Personalized Outfit Generation and Recommendation
The evolution of Outfit Recommendation (OR) in the realm of fashion has progressed through two distinct phases: Pre-defined Outfit Recommendation and Personalized Outfit Composition.
Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
The training samples for the PPO trainer are also the responses generated during the reflective process, which eliminates the need for human annotators.
SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting
Temporal complex event forecasting aims to predict the future events given the observed events from history.
MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation
It does, however, have two limitations: 1) the two-view formulation does not fully exploit all the heterogeneous relations among users, bundles and items; and 2) the "early contrast and late fusion" framework is less effective in capturing user preference and difficult to generalize to multiple views.
Enhancing Item-level Bundle Representation for Bundle Recommendation
In this paper, we propose a novel approach EBRec, short of Enhanced Bundle Recommendation, which incorporates two enhanced modules to explore inherent item-level bundle representations.
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Specifically, we use self-attention modules to combine the multimodal and multi-item features, and then leverage both item- and bundle-level contrastive learning to enhance the representation learning, thus to counter the modality missing, noise, and sparsity problems.
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data.
Context-aware Event Forecasting via Graph Disentanglement
The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future.
Causal Disentangled Recommendation Against User Preference Shifts
Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: 1) capturing the preference shifts across environments for accurate preference prediction, and 2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference.
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively.
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020.
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation
Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce.
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media
Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends.
Rethinking Dialogue State Tracking with Reasoning
Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management.
Knowledge Enhanced Neural Fashion Trend Forecasting
Further-more, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data.
Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media
We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata.
Automatic Fashion Knowledge Extraction from Social Media
Fashion knowledge plays a pivotal role in helping people in their dressing.
Deep Conversational Recommender in Travel
When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries.
