Learning the Compositional Visual Coherence for Complementary Recommendations

Li, Zhi; Wu, Bo; Liu, Qi; Wu, Likang; Zhao, Hongke; Mei, Tao

doi:10.24963/ijcai.2020/489

Cited by 24 publications

(9 citation statements)

References 2 publications

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“…Lee et al [37] proposed Style2Vec to learn and encode the items' visual style into vector representations for the downstream fashion recommendation, i.e., to recommend an outfit of items with good compatibility. Similarly, Yin et al [7] and Li et al [38] learn and utilize visual compatibility relationship between items to enhance fashion recommendation or complementary item recommendation. Hidayati et al [8] try to match clothing style and personal body shape for effective fashion recommendation.…”

Section: Item Visual Appearance Learning In Recommendationmentioning

confidence: 99%

Learning Users’ Visual Preferences for Improving Recommendations

Wang

Yang

et al. 2022

IEEE Access

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Sequential recommender systems (SRSs) aim to predict the next item interest to a user by learning the users' dynamic preferences over items from the sequential user-item interactions. Most of existing SRSs make recommendations by only modeling a user's main preference towards the functions of items, while ignoring the user's auxiliary visual preference towards the appearances and styles of items. Although visual preference is less significant than the main preference, it may still play an important role in most of users' choice on items. On the one hand, a user often prefers to choose the item which matches her/his visual preference well from multiple items with the same function. For example, a lady may choose one clothes whose style suits her best from multiple clothes with the same function. On the other hand, some particular users (e.g., young girls) are usually very concerned about the appearances of some special items (e.g., clothes, jewelry). Therefore, the overlook of modeling users' visual preference may generate unsatisfied recommendations which can not match a user's various types of preferences and thus reduce the consumption experience. To address this gap, in this paper, we propose modeling users' visual preferences to improve the performance of sequential recommendations. Specifically, we devise a coupled Double-chain Preference learning Network (DPN) to jointly learn a user's main preference and visual preference as well as the interactions between them. In DPN, one chain is for modeling a user's main preference by taking the IDs of items as the input and the other chain is for modeling the user's visual preference by taking appearance images of items as the input. Finally, the two types of preferences are carefully integrated with an attention module for the next item prediction. Extensive experiments on two real-world transaction datasets show the superiority of our proposed DPN over the representative and state-of-the-art SRSs. INDEX TERMS visual preference, recommendations, deep neural networks I. INTRODUCTION

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Section: Item Visual Appearance Learning In Recommendationmentioning

confidence: 99%

Learning Users’ Visual Preferences for Improving Recommendations

Wang

Yang

et al. 2022

IEEE Access

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“…Anwaar et al [4] propose an autoencoder based visiolinguistic composition method to achieve a better vision-language composition. To maintain global content coherent of the recommended items with user obtained items, Li et al [23] propose a content attentive composition approach.…”

Section: Related Work 21 Vision-language Retrieval Systemmentioning

confidence: 99%

Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes

2021

Proceedings of the 29th ACM International Conference on Multimedia

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In vision-language retrieval systems, users provide natural language feedback to find target images. Vision-language explanations in the systems can better guide users to provide feedback and thus improve the retrieval. However, developing explainable vision-language retrieval systems can be challenging, due to limited labeled multimodal data. In the retrieval of complex scenes, the issue of limited labeled data can be more severe. With multiple objects in the complex scenes, each user query may not exhaustively describe all objects in the desired image and thus more labeled queries are needed. The issue of limited labeled data can cause data selection biases, and result in spurious correlations learned by the models. When learning spurious correlations, existing explainable models may not be able to accurately extract regions from images and keywords from user queries.In this paper, we discover that deconfounded learning is an important step to provide better vision-language explanations. Thus we propose a deconfounded explainable vision-language retrieval system. By introducing deconfounded learning to pretrain our vision-language model, the spurious correlations in the model can be reduced through interventions by potential confounders. This helps to train more accurate representations and further enable better explainability. Based on explainable retrieval results, we propose novel interactive mechanisms. In such interactions, users can better understand why the system returns particular results and give feedback effectively improving the results. This additional feedback is sample efficient and thus alleviates the data limitation problem. Through extensive experiments, our system achieves about 60% improvements, compared to the state-of-the-art. CCS CONCEPTS• Computing methodologies → Causal reasoning and diagnostics; • Information systems → Users and interactive retrieval; Retrieval efficiency; Presentation of retrieval results; Image search.

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“…Meanwhile, there were other influential semantic learning approaches in some studies. For instance, many models [4,17,25] were utilized to various fields because of their latent semantic analysis ability.…”

Section: Related Workmentioning

confidence: 99%

Learning the Implicit Semantic Representation on Graph-Structured Data

Zhao

et al. 2021

Preprint

Self Cite

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Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited. In this paper, we propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs. In previous work, there are explorations of graph semantics via meta-paths. However, these methods mainly rely on explicit heterogeneous information that is hard to be obtained in a large amount of graph-structured data. SGCN first breaks through this restriction via leveraging the semantic-paths dynamically and automatically during the node aggregating process. To evaluate our idea, we conduct sufficient experiments on several standard datasets, and the empirical results show the superior performance of our model. 4

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Learning the Compositional Visual Coherence for Complementary Recommendations

Cited by 24 publications

References 2 publications

Learning Users’ Visual Preferences for Improving Recommendations

Learning Users’ Visual Preferences for Improving Recommendations

Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes

Learning the Implicit Semantic Representation on Graph-Structured Data

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