A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation

• URL: http://arxiv.org/abs/2004.06651v4
• Date: Sun, 26 Jul 2020 13:03:21 GMT
• Title: A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation
• Authors: Chaoyang Wang and Zhiqiang Guo and Jianjun Li and Peng Pan and Guohui Li
• Abstract summary: We propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for interactive recommender systems (IRSs) Specifically, we leverage textual information to map items and users into a feature space, which greatly alleviates the sparsity problem. We show that TDDPG-Rec achieves state-of-the-art performance over several baselines in a time-efficient manner.
• Score: 15.723042747172688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in interactive recommender systems (IRSs). IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient. Moreover, data sparsity is another challenging problem that most IRSs are confronted with. While the textual information like reviews and descriptions is less sensitive to sparsity, existing RL-based recommendation methods either neglect or are not suitable for incorporating textual information. To address these two problems, in this paper, we propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for IRSs. Specifically, we leverage textual information to map items and users into a feature space, which greatly alleviates the sparsity problem. Moreover, we design an effective method to construct an action candidate set. By the policy vector dynamically learned from TDDPG-Rec that expresses the user's preference, we can select actions from the candidate set effectively. Through experiments on three public datasets, we demonstrate that TDDPG-Rec achieves state-of-the-art performance over several baselines in a time-efficient manner.

Related papers

• Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation [51.06031200728449]
  We propose a novel framework called mccHRL to provide different levels of temporal abstraction on listwise recommendation. Within the hierarchical framework, the high-level agent studies the evolution of user perception, while the low-level agent produces the item selection policy. Results observe significant performance improvement by our method, compared with several well-known baselines.
  arXiv  Detail & Related papers  (2024-09-11T17:01:06Z)
• An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation [14.506332665769746]
  We propose an underlinetextbfEfficient underlinetextbfContinuous underlinetextbfControl framework (ECoC) Based on a statistically tested assumption, we first propose the novel unified action representation abstracted from normalized user and item spaces. During this process, strategic exploration and directional control in terms of unified actions are carefully designed and crucial to final recommendation decisions.
  arXiv  Detail & Related papers  (2024-08-15T09:26:26Z)
• A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems [67.52782366565658]
  State-of-the-art recommender systems (RSs) depend on categorical features, which ecoded by embedding vectors, resulting in excessively large embedding tables. Despite the prosperity of lightweight embedding-based RSs, a wide diversity is seen in evaluation protocols. This study investigates various LERS' performance, efficiency, and cross-task transferability via a thorough benchmarking process.
  arXiv  Detail & Related papers  (2024-06-25T07:45:00Z)
• ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems [40.838320650137625]
  This paper presents ERASE, a comprehensive bEnchmaRk for feAture SElection for Deep Recommender Systems (DRS) ERASE comprises a thorough evaluation of eleven feature selection methods, covering both traditional and deep learning approaches. Our code is available online for ease of reproduction.
  arXiv  Detail & Related papers  (2024-03-19T11:49:35Z)
• Representation Learning with Large Language Models for Recommendation [34.46344639742642]
  We propose a model-agnostic framework RLMRec to enhance recommenders with large language models (LLMs)empowered representation learning. RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals.
  arXiv  Detail & Related papers  (2023-10-24T15:51:13Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Conversational Recommendation as Retrieval: A Simple, Strong Baseline [4.737923227003888]
  Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation. Most CRS approaches do not effectively utilize the signal provided by these conversations. We propose an alternative information retrieval (IR)-styled approach to the CRS item recommendation task.
  arXiv  Detail & Related papers  (2023-05-23T06:21:31Z)
• Towards Topic-Guided Conversational Recommender System [80.3725246715938]
  We contribute a new CRS dataset named textbfTG-ReDial (textbfRecommendation through textbfTopic-textbfGuided textbfDialog) We present the task of topic-guided conversational recommendation, and propose an effective approach to this task.
  arXiv  Detail & Related papers  (2020-10-08T17:04:30Z)
• Leveraging Historical Interaction Data for Improving Conversational Recommender System [105.90963882850265]
  We propose a novel pre-training approach to integrate item- and attribute-based preference sequence. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-08-19T03:43:50Z)
• Critic Regularized Regression [70.8487887738354]
  We propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR) We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2020-06-26T17:50:26Z)

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.