SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation

• URL: http://arxiv.org/abs/2408.08686v2
• Date: Mon, 19 Aug 2024 04:31:51 GMT
• Title: SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation
• Authors: Tongyoung Kim, Soojin Yoon, Seongku Kang, Jinyoung Yeo, Dongha Lee,
• Abstract summary: We propose SC-Rec, a unified recommender system that learns diverse preference knowledge from two distinct item indices and multiple prompt templates. SC-Rec considerably outperforms the state-of-the-art methods for sequential recommendation, effectively incorporating complementary knowledge from varied outputs of the model.
• Score: 18.519480704213017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language Models (LMs) are increasingly employed in recommendation systems due to their advanced language understanding and generation capabilities. Recent recommender systems based on generative retrieval have leveraged the inferential abilities of LMs to directly generate the index tokens of the next item, based on item sequences within the user's interaction history. Previous studies have mostly focused on item indices based solely on textual semantic or collaborative information. However, although the standalone effectiveness of these aspects has been demonstrated, the integration of this information has remained unexplored. Our in-depth analysis finds that there is a significant difference in the knowledge captured by the model from heterogeneous item indices and diverse input prompts, which can have a high potential for complementarity. In this paper, we propose SC-Rec, a unified recommender system that learns diverse preference knowledge from two distinct item indices and multiple prompt templates. Furthermore, SC-Rec adopts a novel reranking strategy that aggregates a set of ranking results, inferred based on different indices and prompts, to achieve the self-consistency of the model. Our empirical evaluation on three real-world datasets demonstrates that SC-Rec considerably outperforms the state-of-the-art methods for sequential recommendation, effectively incorporating complementary knowledge from varied outputs of the model.

Related papers

• Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models [17.202017214385826]
  This study evaluates the instruction-following capabilities of various retrieval models beyond content relevance. We develop a novel retrieval evaluation benchmark spanning six document-level attributes. Our findings reveal that while reranking models generally surpass retrieval models in instruction following, they still face challenges in handling certain attributes.
  arXiv  Detail & Related papers  (2024-10-31T11:47:21Z)
• Beyond Retrieval: Generating Narratives in Conversational Recommender Systems [4.912663905306209]
  We introduce a new dataset (REGEN) for natural language generation tasks in conversational recommendations. We establish benchmarks using well-known generative metrics, and perform an automated evaluation of the new dataset using a rater LLM. And to the best of our knowledge, represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives.
  arXiv  Detail & Related papers  (2024-10-22T07:53:41Z)
• Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator [60.07198935747619]
  We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
  arXiv  Detail & Related papers  (2024-09-14T01:45:04Z)
• Exploring Information Retrieval Landscapes: An Investigation of a Novel Evaluation Techniques and Comparative Document Splitting Methods [0.0]
  In this study, the structured nature of textbooks, the conciseness of articles, and the narrative complexity of novels are shown to require distinct retrieval strategies. A novel evaluation technique is introduced, utilizing an open-source model to generate a comprehensive dataset of question-and-answer pairs. The evaluation employs weighted scoring metrics, including SequenceMatcher, BLEU, METEOR, and BERT Score, to assess the system's accuracy and relevance.
  arXiv  Detail & Related papers  (2024-09-13T02:08:47Z)
• UserSumBench: A Benchmark Framework for Evaluating User Summarization Approaches [25.133460380551327]
  Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user information such as preferences and interests, and are invaluable for personalization applications. However, the development of new summarization techniques is hindered by the lack of ground-truth labels, the inherent subjectivity of user summaries, and human evaluation.
  arXiv  Detail & Related papers  (2024-08-30T01:56:57Z)
• Hierarchical Indexing for Retrieval-Augmented Opinion Summarization [60.5923941324953]
  We propose a method for unsupervised abstractive opinion summarization that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs) Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews.
  arXiv  Detail & Related papers  (2024-03-01T10:38:07Z)
• Enhancing Few-shot NER with Prompt Ordering based Data Augmentation [59.69108119752584]
  We propose a Prompt Ordering based Data Augmentation (PODA) method to improve the training of unified autoregressive generation frameworks. Experimental results on three public NER datasets and further analyses demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-05-19T16:25:43Z)
• Recommender Systems with Generative Retrieval [58.454606442670034]
  We propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful of codewords to serve as a Semantic ID for each item. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets.
  arXiv  Detail & Related papers  (2023-05-08T21:48:17Z)
• SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval [11.38022203865326]
  SPLADE model provides highly sparse representations and competitive results with respect to state-of-the-art dense and sparse approaches. We modify the pooling mechanism, benchmark a model solely based on document expansion, and introduce models trained with distillation. Overall, SPLADE is considerably improved with more than $9$% gains on NDCG@10 on TREC DL 2019, leading to state-of-the-art results on the BEIR benchmark.
  arXiv  Detail & Related papers  (2021-09-21T10:43:42Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

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.