Learning to Ask: Conversational Product Search via Representation Learning

• URL: http://arxiv.org/abs/2411.14466v1
• Date: Mon, 18 Nov 2024 14:05:43 GMT
• Title: Learning to Ask: Conversational Product Search via Representation Learning
• Authors: Jie Zou, Jimmy Xiangji Huang, Zhaochun Ren, Evangelos Kanoulas,
• Abstract summary: We propose a new conversational product search model, ConvPS, to assist users in locating desirable items. The model is first trained to jointly learn the semantic representations of user, query, item, and conversation. Our proposed ConvPS model can naturally integrate the representation learning of the user, query, item, and conversation into a unified generative framework.
• Score: 34.689904564411506
• License:
• Abstract: Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from traditional product search to conversational product search. Conversational product search enables user-machine conversations and through them collects explicit user feedback that allows to actively clarify the users' product preferences. Therefore, prospective research on an intelligent shopping assistant via conversations is indispensable. Existing publications on conversational product search either model conversations independently from users, queries, and products or lead to a vocabulary mismatch. In this work, we propose a new conversational product search model, ConvPS, to assist users in locating desirable items. The model is first trained to jointly learn the semantic representations of user, query, item, and conversation via a unified generative framework. After learning these representations, they are integrated to retrieve the target items in the latent semantic space. Meanwhile, we propose a set of greedy and explore-exploit strategies to learn to ask the user a sequence of high-performance questions for conversations. Our proposed ConvPS model can naturally integrate the representation learning of the user, query, item, and conversation into a unified generative framework, which provides a promising avenue for constructing accurate and robust conversational product search systems that are flexible and adaptive. Experimental results demonstrate that our ConvPS model significantly outperforms state-of-the-art baselines.

Related papers

• A Survey of Conversational Search [44.09402706387407]
  We explore the recent advancements and potential future directions in conversational search. We highlight the integration of large language models (LLMs) in enhancing these systems. We provide insights into real-world applications and robust evaluations of current conversational search systems.
  arXiv  Detail & Related papers  (2024-10-21T01:54:46Z)
• Using LLMs to Investigate Correlations of Conversational Follow-up Queries with User Satisfaction [32.32365329050603]
  We propose a taxonomy of 18 users' follow-up query patterns from conversational search engine Naver Cue:. Compared to the existing literature on query reformulations, we uncovered a new set of motivations and actions behind follow-up queries. Our initial findings suggest some signals of dissatisfactions, such as Clarifying Queries, Excluding Condition, and Substituting Condition with follow-up queries.
  arXiv  Detail & Related papers  (2024-07-18T05:10:35Z)
• ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions [68.81939215223818]
  ProductAgent is a conversational information seeking agent equipped with abilities of strategic clarification question generation and dynamic product retrieval. We develop the agent with strategies for product feature summarization, query generation, and product retrieval. Experiments show that ProductAgent interacts positively with the user and enhances retrieval performance with increasing dialogue turns.
  arXiv  Detail & Related papers  (2024-07-01T03:50:23Z)
• Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations [66.16863141262506]
  We present a novel approach that focuses on generating internet search queries guided by social commonsense. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation.
  arXiv  Detail & Related papers  (2023-10-22T16:14:56Z)
• SSP: Self-Supervised Post-training for Conversational Search [63.28684982954115]
  We propose fullmodel (model) which is a new post-training paradigm with three self-supervised tasks to efficiently initialize the conversational search model. To verify the effectiveness of our proposed method, we apply the conversational encoder post-trained by model on the conversational search task using two benchmark datasets: CAsT-19 and CAsT-20.
  arXiv  Detail & Related papers  (2023-07-02T13:36:36Z)
• Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation [62.019437228000776]
  We present TalkWalk, which generates realistic high-quality conversational data by leveraging encoded expertise in widely available item collections. We generate over one million diverse conversations in a human-collected dataset.
  arXiv  Detail & Related papers  (2023-01-27T01:54:16Z)
• End-to-End Conversational Search for Online Shopping with Utterance Transfer [42.18467682958695]
  We first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. We then address the lack of data challenges by proposing an utterance transfer approach. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing.
  arXiv  Detail & Related papers  (2021-09-12T08:33:44Z)
• Conversations with Search Engines: SERP-based Conversational Response Generation [77.1381159789032]
  We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines. We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
  arXiv  Detail & Related papers  (2020-04-29T13:07:53Z)
• Modeling Product Search Relevance in e-Commerce [7.139647051098728]
  We propose a robust way of predicting relevance scores given a search query and a product. We compare conventional information retrieval models such as BM25 and Indri with deep learning models such as word2vec, sentence2vec and paragraph2vec.
  arXiv  Detail & Related papers  (2020-01-14T21:17:55Z)

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.