FineRec:Exploring Fine-grained Sequential Recommendation
- URL: http://arxiv.org/abs/2404.12975v1
- Date: Fri, 19 Apr 2024 16:04:26 GMT
- Title: FineRec:Exploring Fine-grained Sequential Recommendation
- Authors: Xiaokun Zhang, Bo Xu, Youlin Wu, Yuan Zhong, Hongfei Lin, Fenglong Ma,
- Abstract summary: 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.
- Score: 28.27273649170967
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. 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. To tackle the diversity of opinions, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several realworld datasets demonstrate the superiority of our FineRec over existing state-of-the-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.
Related papers
- Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential
Recommendations [50.03560306423678]
We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems.
Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
arXiv Detail & Related papers (2024-01-12T15:26:40Z) - 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) - Graph-based Extractive Explainer for Recommendations [38.278148661173525]
We develop a graph attentive neural network model that seamlessly integrates user, item, attributes, and sentences for extraction-based explanation.
To balance individual sentence relevance, overall attribute coverage, and content redundancy, we solve an integer linear programming problem to make the final selection of sentences.
arXiv Detail & Related papers (2022-02-20T04:56:10Z) - Discovering Personalized Semantics for Soft Attributes in Recommender
Systems using Concept Activation Vectors [34.56323846959459]
Interactive recommender systems allow users to express intent, preferences, constraints, and contexts in a richer fashion.
One challenge is inferring a user's semantic intent from the open-ended terms or attributes often used to describe a desired item.
We develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in recommender systems.
arXiv Detail & Related papers (2022-02-06T18:45:15Z) - 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) - Attribute-aware Explainable Complementary Clothing Recommendation [37.30129304097086]
This work aims to tackle the explainability challenge in fashion recommendation tasks by proposing a novel Attribute-aware Fashion Recommender (AFRec)
AFRec recommender assesses the outfit compatibility by explicitly leveraging the extracted attribute-level representations from each item's visual feature.
The attributes serve as the bridge between two fashion items, where we quantify the affinity of a pair of items through the learned compatibility between their attributes.
arXiv Detail & Related papers (2021-07-04T14:56:07Z) - Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback
based Recommendation [59.183016033308014]
In this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation.
Our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches.
arXiv Detail & Related papers (2021-05-16T08:06:22Z) - 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) - Seamlessly Unifying Attributes and Items: Conversational Recommendation
for Cold-Start Users [111.28351584726092]
We consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.
Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play.
arXiv Detail & Related papers (2020-05-23T08:56:37Z)
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.