Explainable Session-based Recommendation via Path Reasoning

• URL: http://arxiv.org/abs/2403.00832v1
• Date: Wed, 28 Feb 2024 12:11:08 GMT
• Title: Explainable Session-based Recommendation via Path Reasoning
• Authors: Yang Cao, Shuo Shang, Jun Wang, and Wei Zhang
• Abstract summary: We propose a hierarchical reinforcement learning framework for SR, which improves the explainability of existing SR models via Path Reasoning, namely PR4SR. Considering the different importance of items to the session, we design the session-level agent to select the items in the session as the starting point for path reasoning and the path-level agent to perform path reasoning. In particular, we design a multi-target reward mechanism to adapt to the skip behaviors of sequential patterns in SR, and introduce path midpoint reward to enhance the exploration efficiency in knowledge graphs.
• Score: 27.205463326317656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores providing explainability for session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditional path reasoning prioritizes knowledge graph exploration, ignoring sequential patterns present in the session history. Therefore, we propose a generalized hierarchical reinforcement learning framework for SR, which improves the explainability of existing SR models via Path Reasoning, namely PR4SR. Considering the different importance of items to the session, we design the session-level agent to select the items in the session as the starting point for path reasoning and the path-level agent to perform path reasoning. In particular, we design a multi-target reward mechanism to adapt to the skip behaviors of sequential patterns in SR, and introduce path midpoint reward to enhance the exploration efficiency in knowledge graphs. To improve the completeness of the knowledge graph and to diversify the paths of explanation, we incorporate extracted feature information from images into the knowledge graph. We instantiate PR4SR in five state-of-the-art SR models (i.e., GRU4REC, NARM, GCSAN, SR-GNN, SASRec) and compare it with other explainable SR frameworks, to demonstrate the effectiveness of PR4SR for recommendation and explanation tasks through extensive experiments with these approaches on four datasets.

Related papers

• Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models [62.12031550252253]
  We present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning. PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers.
  arXiv  Detail & Related papers  (2024-12-23T20:27:12Z)
• Pay Attention to Attention for Sequential Recommendation [8.228670498220016]
  Transformer-based approaches have demonstrated remarkable success in various sequence-based tasks. We propose a novel sequential recommendation (SR) approach called attention weight refinement (AWRR) AWRR pays attention to attention weights, allowing for more refined attention distributions of correlations among items. We conduct comprehensive experiments on multiple real-world datasets, demonstrating that our approach consistently outperforms state-of-the-art SR models.
  arXiv  Detail & Related papers  (2024-10-28T14:03:39Z)
• Graph and Sequential Neural Networks in Session-based Recommendation: A Survey [41.59094128068782]
  Session-based recommendation (SR) specializes in users' short-term preference capture and aims to provide a more dynamic and timely recommendation. First, we clarify the definitions of various SR tasks and introduce the characteristics of session-based recommendation. Second, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods.
  arXiv  Detail & Related papers  (2024-08-27T08:08:05Z)
• DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential Recommendation [7.914816884185941]
  Sequential recommendation (SR) tasks aim to predict users' next interaction by learning their behavior sequence and capturing the connection between users' past interactions and their changing preferences. Conventional SR models often focus solely on capturing sequential patterns within the training data, neglecting the broader context and semantic information embedded in item titles from external sources. Large language models (LLMs) have recently shown promise in SR tasks due to their advanced understanding capabilities and strong generalization abilities.
  arXiv  Detail & Related papers  (2024-06-17T02:47:09Z)
• RouteExplainer: An Explanation Framework for Vehicle Routing Problem [1.7034420812099471]
  We propose RouteExplainer, a post-hoc explanation framework that explains the influence of each edge in a generated route. Our framework realizes this by rethinking a route as the sequence of actions and extending counterfactual explanations based on the action influence model to VRP. To enhance the explanation, we additionally propose an edge classifier that infers the intentions of each edge, a loss function to train the edge classifier, and explanation-text generation by Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-03-06T10:01:35Z)
• Deep Reinforcement Learning from Hierarchical Preference Design [99.46415116087259]
  This paper shows by exploiting certain structures, one can ease the reward design process. We propose a hierarchical reward modeling framework -- HERON for scenarios: (I) The feedback signals naturally present hierarchy; (II) The reward is sparse, but with less important surrogate feedback to help policy learning.
  arXiv  Detail & Related papers  (2023-09-06T00:44:29Z)
• Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation [49.85548436111153]
  We propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC) SRC formulates the recommendation task under a set-to-sequence paradigm. We conduct extensive experiments on two real-world public datasets and one industrial dataset.
  arXiv  Detail & Related papers  (2023-06-07T08:24:44Z)
• RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection [66.08293086254851]
  We propose a reciprocal learning framework to reinforce the learning of a RefSR network. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm.
  arXiv  Detail & Related papers  (2022-11-08T12:39:35Z)
• Intent Contrastive Learning for Sequential Recommendation [86.54439927038968]
  We introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm.
  arXiv  Detail & Related papers  (2022-02-05T09:24:13Z)
• Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation [57.67616822888859]
  We propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL) TMER-RL utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.
  arXiv  Detail & Related papers  (2021-11-24T04:34:26Z)

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.