Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation

• URL: http://arxiv.org/abs/2407.00912v1
• Date: Mon, 01 Jul 2024 02:36:03 GMT
• Title: Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation
• Authors: Yuting Zhang, Yiqing Wu, Ruidong Han, Ying Sun, Yongchun Zhu, Xiang Li, Wei Lin, Fuzhen Zhuang, Zhulin An, Yongjun Xu,
• Abstract summary: We propose a novel model named Unified Dual-Intents Translation for joint modeling of Search and Recommendation (UDITSR) To accurately simulate users' demand intents in recommendation, we utilize real queries from search data as supervision information to guide its generation. Extensive experiments demonstrate that UDITSR outperforms SOTA baselines both in search and recommendation tasks.
• Score: 44.59113848489519
• License:
• Abstract: Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging inherent intents (e.g., always preferring high-quality items) and changing demand intents (e.g., wanting a T-shirt in summer but a down jacket in winter). However, both types of intents are implicitly expressed in recommendation scenario, posing challenges in leveraging them for accurate intent-aware recommendations. Fortunately, in search scenario, often found alongside recommendation on the same online platform, users express their demand intents explicitly through their query words. Intuitively, in both scenarios, a user shares the same inherent intent and the interactions may be influenced by the same demand intent. It is therefore feasible to utilize the interaction data from both scenarios to reinforce the dual intents for joint intent-aware modeling. But the joint modeling should deal with two problems: 1) accurately modeling users' implicit demand intents in recommendation; 2) modeling the relation between the dual intents and the interactive items. To address these problems, we propose a novel model named Unified Dual-Intents Translation for joint modeling of Search and Recommendation (UDITSR). To accurately simulate users' demand intents in recommendation, we utilize real queries from search data as supervision information to guide its generation. To explicitly model the relation among the triplet <inherent intent, demand intent, interactive item>, we propose a dual-intent translation propagation mechanism to learn the triplet in the same semantic space via embedding translations. Extensive experiments demonstrate that UDITSR outperforms SOTA baselines both in search and recommendation tasks.

Related papers

• Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering [9.740376003100437]
  We propose a novel recommendation framework designated as Bilateral Intent-guided Graph Collaborative Filtering (BIGCF) Specifically, we take a closer look at user-item interactions from a causal perspective and put forth the concepts of individual intent. To counter the sparsity of implicit feedback, the feature distributions of users and items are encoded via a Gaussian-based graph generation strategy.
  arXiv  Detail & Related papers  (2024-05-15T02:31:26Z)
• Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders [24.42199777529863]
  Large Language Models (LLMs) enable embeddings allowing one to adjust semantics over the embedding space using prompts. Traditional evaluation benchmarks rely solely on task metrics that don't particularly measure gaps related to semantic understanding. We propose an intent semantic toolkit that gives a more holistic view of intent embedding models.
  arXiv  Detail & Related papers  (2024-03-07T08:32:17Z)
• Co-guiding for Multi-intent Spoken Language Understanding [53.30511968323911]
  We propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the mutual guidances between the two tasks. For the first stage, we propose single-task supervised contrastive learning, and for the second stage, we propose co-guiding supervised contrastive learning. Experiment results on multi-intent SLU show that our model outperforms existing models by a large margin.
  arXiv  Detail & Related papers  (2023-11-22T08:06:22Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence [36.91974576050925]
  We argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks.
  arXiv  Detail & Related papers  (2021-09-30T11:06:15Z)
• From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users [3.464871689508835]
  Implicit and explicit feedback have different roles for a useful recommendation. We go from the hypothesis that a user's preference at a time is a combination of long-term and short-term interests.
  arXiv  Detail & Related papers  (2021-07-26T16:59:20Z)
• Dual Side Deep Context-aware Modulation for Social Recommendation [50.59008227281762]
  We propose a novel graph neural network to model the social relation and collaborative relation. On top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
  arXiv  Detail & Related papers  (2021-03-16T11:08:30Z)
• User Intent Inference for Web Search and Conversational Agents [3.9400263964632836]
  thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain.
  arXiv  Detail & Related papers  (2020-05-28T07:04:42Z)
• AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling [69.59096090788125]
  In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling. We introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information.
  arXiv  Detail & Related papers  (2020-04-21T15:07:34Z)
• IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems [80.0781718687327]
  We analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART" IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture.
  arXiv  Detail & Related papers  (2020-02-03T05:59:52Z)

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.