BeFA: A General Behavior-driven Feature Adapter for Multimedia Recommendation
- URL: http://arxiv.org/abs/2406.00323v2
- Date: Mon, 13 Jan 2025 05:39:04 GMT
- Title: BeFA: A General Behavior-driven Feature Adapter for Multimedia Recommendation
- Authors: Qile Fan, Penghang Yu, Zhiyi Tan, Bing-Kun Bao, Guanming Lu,
- Abstract summary: Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences.
Pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details.
We propose an effective and efficient general Behavior-driven Feature Adapter (BeFA) to tackle these issues.
- Score: 3.956286230894268
- License:
- Abstract: Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them with behavioral features. However, pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details. We speculate that it may result in the extracted features not containing sufficient features to accurately reflect user preferences. To verify our hypothesis, we introduce an attribution analysis method for visually and intuitively analyzing the content features. The results indicate that certain products' content features exhibit the issues of information drift}and information omission,reducing the expressive ability of features. Building upon this finding, we propose an effective and efficient general Behavior-driven Feature Adapter (BeFA) to tackle these issues. This adapter reconstructs the content feature with the guidance of behavioral information, enabling content features accurately reflecting user preferences. Extensive experiments demonstrate the effectiveness of the adapter across all multimedia recommendation methods. Our code is made publicly available on https://github.com/fqldom/BeFA.
Related papers
- Were You Helpful -- Predicting Helpful Votes from Amazon Reviews [0.0]
This project investigates factors that influence the perceived helpfulness of Amazon product reviews through machine learning techniques.
We identify key metadata characteristics that serve as strong predictors of review helpfulness.
This insight suggests that contextual and user-behavioral factors may be more indicative of review helpfulness than the linguistic content itself.
arXiv Detail & Related papers (2024-12-03T22:38:58Z) - Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation [19.76299850698492]
We propose a Counterfactual learning-driven representation disentanglement framework for search-enhanced recommendation.
We leverage search queries to construct counterfactual signals to disentangle item representations, isolating only query-independent general features.
Experiments on real datasets demonstrate ClardRec is effective in both collaborative filtering and sequential recommendation scenarios.
arXiv Detail & Related papers (2024-11-14T09:51:50Z) - FineRec:Exploring Fine-grained Sequential Recommendation [28.27273649170967]
We propose a novel framework that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation.
For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes.
We present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations.
arXiv Detail & Related papers (2024-04-19T16:04:26Z) - Enhancing Content-based Recommendation via Large Language Model [19.005906480699334]
We propose a semantic knowledge transferring method textbfLoID, which includes two major components.
We conduct extensive experiments with SOTA baselines on real-world datasets, the detailed results demonstrating significant improvements of our method LoID.
arXiv Detail & Related papers (2024-03-30T03:56:53Z) - Enhancing User Intent Capture in Session-Based Recommendation with
Attribute Patterns [77.19390850643944]
We propose the Frequent Attribute Pattern Augmented Transformer (FAPAT)
FAPAT characterizes user intents by building attribute transition graphs and matching attribute patterns.
We demonstrate that FAPAT consistently outperforms state-of-the-art methods by an average of 4.5% across various evaluation metrics.
arXiv Detail & Related papers (2023-12-23T03:28:18Z) - Video Infringement Detection via Feature Disentanglement and Mutual
Information Maximization [51.206398602941405]
We propose to disentangle an original high-dimensional feature into multiple sub-features.
On top of the disentangled sub-features, we learn an auxiliary feature to enhance the sub-features.
Our method achieves 90.1% TOP-100 mAP on the large-scale SVD dataset and also sets the new state-of-the-art on the VCSL benchmark dataset.
arXiv Detail & Related papers (2023-09-13T10:53:12Z) - Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention [52.84233165201391]
We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched.
Our method explicitly leverages the contents of both the preferred clips and the target videos using pre-trained features for the objects and the human activities.
arXiv Detail & Related papers (2022-07-18T02:32:48Z) - Knowledge-Enhanced Hierarchical Graph Transformer Network for
Multi-Behavior Recommendation [56.12499090935242]
This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems.
KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics.
We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
arXiv Detail & Related papers (2021-10-08T09:44:00Z) - SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item
Recommendation [48.1799451277808]
We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation.
We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
arXiv Detail & Related papers (2021-08-18T08:04:38Z) - Joint Item Recommendation and Attribute Inference: An Adaptive Graph
Convolutional Network Approach [61.2786065744784]
In recommender systems, users and items are associated with attributes, and users show preferences to items.
As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values.
We propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference.
arXiv Detail & Related papers (2020-05-25T10:50:01Z)
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.