Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation

• URL: http://arxiv.org/abs/2104.12483v1
• Date: Mon, 26 Apr 2021 11:28:28 GMT
• Title: Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation
• Authors: Yinjiang Cai, Zeyu Cui, Shu Wu, Zhen Lei, Xibo Ma
• Abstract summary: Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising. Recent models use methods such as attention mechanism and deep neural network to learn the user representation and scoring function more accurately. We propose an enhanced representation of the target item which distills relevant information from the co-occurrence items.
• Score: 37.28220632871373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising. It models users' preference on target items by the items they have interacted with. Recent models use methods such as attention mechanism and deep neural network to learn the user representation and scoring function more accurately. However, despite their effectiveness, such models still overlook a problem that performance of ICF methods heavily depends on the quality of item representation especially the target item representation. In fact, due to the long-tail distribution in the recommendation, most item embeddings can not represent the semantics of items accurately and thus degrade the performance of current ICF methods. In this paper, we propose an enhanced representation of the target item which distills relevant information from the co-occurrence items. We design sampling strategies to sample fix number of co-occurrence items for the sake of noise reduction and computational cost. Considering the different importance of sampled items to the target item, we apply attention mechanism to selectively adopt the semantic information of the sampled items. Our proposed Co-occurrence based Enhanced Representation model (CER) learns the scoring function by a deep neural network with the attentive user representation and fusion of raw representation and enhanced representation of target item as input. With the enhanced representation, CER has stronger representation power for the tail items compared to the state-of-the-art ICF methods. Extensive experiments on two public benchmarks demonstrate the effectiveness of CER.

Related papers

• Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models [50.370043676415875]
  In smart retail applications, the large number of products and their frequent turnover necessitate reliable zero-shot object classification methods. We introduce the MIMEX dataset, comprising 28 distinct product categories. We benchmark the zero-shot object classification performance of state-of-the-art vision-language models (VLMs) on the proposed MIMEX dataset.
  arXiv  Detail & Related papers  (2024-09-23T12:28:40Z)
• A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings [4.989653738257287]
  Interest-aware Capsule network (IaCN) is a model-agnostic framework that directly learns interest-oriented item representations. IaCN serves as an auxiliary task, enabling the joint learning of both item-based and interest-based representations. We evaluate the proposed approach on benchmark datasets, exploring various scenarios involving different deep neural networks.
  arXiv  Detail & Related papers  (2023-08-17T22:40:59Z)
• Deep Context Interest Network for Click-Through Rate Prediction [16.37806484383383]
  We propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.
  arXiv  Detail & Related papers  (2023-08-11T09:32:58Z)
• Towards Explainable Collaborative Filtering with Taste Clusters Learning [43.4512681951459]
  Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. Adding explainability to recommendation models can not only increase trust in the decisionmaking process, but also have multiple benefits. We propose a neat and effective Explainable Collaborative Filtering (ECF) model that leverages interpretable cluster learning.
  arXiv  Detail & Related papers  (2023-04-27T03:08:15Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• Semantic Representation and Dependency Learning for Multi-Label Image Recognition [76.52120002993728]
  We propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions.
  arXiv  Detail & Related papers  (2022-04-08T00:55:15Z)
• CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention [7.573586022424399]
  In recommender settings, users' context and item attributes play a crucial role in deciding which items to recommend next. We propose a context and attribute-aware recommender model (CARCA) that can capture the dynamic nature of the user profiles in terms of contextual features and item attributes. Experiments on four real-world recommender system datasets show that the proposed model significantly outperforms all state-of-the-art models in the task of item recommendation.
  arXiv  Detail & Related papers  (2022-04-04T13:22:28Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Disentangled Graph Collaborative Filtering [100.26835145396782]
  Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data. By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
  arXiv  Detail & Related papers  (2020-07-03T15:37:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.