Enhancing Content-based Recommendation via Large Language Model

• URL: http://arxiv.org/abs/2404.00236v2
• Date: Sun, 28 Jul 2024 01:02:21 GMT
• Title: Enhancing Content-based Recommendation via Large Language Model
• Authors: Wentao Xu, Qianqian Xie, Shuo Yang, Jiangxia Cao, Shuchao Pang,
• Abstract summary: We propose a semantic knowledge transferring method textbfLoID, which includes two major components. We conduct extensive experiments with SOTA baselines on real-world datasets, the detailed results demonstrating significant improvements of our method LoID.
• Score: 19.005906480699334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world applications, users express different behaviors when they interact with different items, including implicit click/like interactions, and explicit comments/reviews interactions. Nevertheless, almost all recommender works are focused on how to describe user preferences by the implicit click/like interactions, to find the synergy of people. For the content-based explicit comments/reviews interactions, some works attempt to utilize them to mine the semantic knowledge to enhance recommender models. However, they still neglect the following two points: (1) The content semantic is a universal world knowledge; how do we extract the multi-aspect semantic information to empower different domains? (2) The user/item ID feature is a fundamental element for recommender models; how do we align the ID and content semantic feature space? In this paper, we propose a `plugin' semantic knowledge transferring method \textbf{LoID}, which includes two major components: (1) LoRA-based large language model pretraining to extract multi-aspect semantic information; (2) ID-based contrastive objective to align their feature spaces. We conduct extensive experiments with SOTA baselines on real-world datasets, the detailed results demonstrating significant improvements of our method LoID.

Related papers

• Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets [9.78470355087662]
  In spoken Task-Oriented Dialogue (TOD) systems, the choice of the semantic representation describing the users' requests is key to a smooth interaction. This paper provides insights into automatic enhancement of spoken dialogue datasets' semantic representations.
  arXiv  Detail & Related papers  (2024-06-19T06:59:57Z)
• DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking [16.728006492769666]
  We propose DWE+ for multimodal entity linking. DWE+ could capture finer semantics and dynamically maintain semantic consistency with entities. Experiments on Wikimel, Richpedia, and Wikidiverse datasets demonstrate the effectiveness of DWE+ in improving MEL performance.
  arXiv  Detail & Related papers  (2024-04-07T05:56:42Z)
• Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification [78.52704557647438]
  We propose a novel FIne-grained Representation and Recomposition (FIRe$2$) framework to tackle both limitations without any auxiliary annotation or data. Experiments demonstrate that FIRe$2$ can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks.
  arXiv  Detail & Related papers  (2023-08-21T12:59:48Z)
• Improving Items and Contexts Understanding with Descriptive Graph for Conversational Recommendation [4.640835690336652]
  State-of-the-art methods on conversational recommender systems (CRS) leverage external knowledge to enhance both items' and contextual words' representations. We propose a new CRS framework KLEVER, which jointly models items and their associated contextual words in the same semantic space. Experiments on benchmarking CRS dataset demonstrate that KLEVER achieves superior performance, especially when the information from the users' responses is lacking.
  arXiv  Detail & Related papers  (2023-04-11T21:21:46Z)
• Position-Aware Contrastive Alignment for Referring Image Segmentation [65.16214741785633]
  We present a position-aware contrastive alignment network (PCAN) to enhance the alignment of multi-modal features. Our PCAN consists of two modules: 1) Position Aware Module (PAM), which provides position information of all objects related to natural language descriptions, and 2) Contrastive Language Understanding Module (CLUM), which enhances multi-modal alignment.
  arXiv  Detail & Related papers  (2022-12-27T09:13:19Z)
• Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media [11.235498285650142]
  Named Entity Recognition (NER) on social media refers to discovering and classifying entities from unstructured free-form content. This work introduces the multi-granularity cross-modality representation learning. Experiments show that our proposed approach can achieve the SOTA or approximate SOTA performance on two benchmark datasets of tweets.
  arXiv  Detail & Related papers  (2022-10-19T15:14:55Z)
• Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition [80.74495836502919]
  In this work, we focus on joint human fashion segmentation and attribute recognition. We introduce the object query for segmentation and the attribute query for attribute prediction. For attribute stream, we design a novel Multi-Layer Rendering module to explore more fine-grained features.
  arXiv  Detail & Related papers  (2022-04-10T11:11:10Z)
• Referring Image Segmentation via Cross-Modal Progressive Comprehension [94.70482302324704]
  Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities. We propose a Cross-Modal Progressive (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task.
  arXiv  Detail & Related papers  (2020-10-01T16:02:30Z)
• Abstractive Summarization of Spoken and Written Instructions with BERT [66.14755043607776]
  We present the first application of the BERTSum model to conversational language. We generate abstractive summaries of narrated instructional videos across a wide variety of topics. We envision this integrated as a feature in intelligent virtual assistants, enabling them to summarize both written and spoken instructional content upon request.
  arXiv  Detail & Related papers  (2020-08-21T20:59:34Z)
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion [77.21442487537139]
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.
  arXiv  Detail & Related papers  (2020-07-08T11:14:23Z)

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.