Context-Aware Service Recommendation System for the Social Internet of Things

• URL: http://arxiv.org/abs/2308.08499v1
• Date: Mon, 14 Aug 2023 14:40:13 GMT
• Title: Context-Aware Service Recommendation System for the Social Internet of Things
• Authors: Amar Khelloufi, Huansheng Ning, Abdelkarim Ben Sada, Abdenacer Naouri and Sahraoui Dhelim
• Abstract summary: Social Internet of Things (SIoT) enables interconnected smart devices to share data and services, opening up opportunities for personalized service recommendations. Existing techniques tend to consider the extraction of social relationships between devices and neglect the contextual presentation of service reviews. This study aims to address these gaps by exploring the contextual representation of each device-service pair.
• Score: 3.0748861313823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Social Internet of Things (SIoT) enables interconnected smart devices to share data and services, opening up opportunities for personalized service recommendations. However, existing research often overlooks crucial aspects that can enhance the accuracy and relevance of recommendations in the SIoT context. Specifically, existing techniques tend to consider the extraction of social relationships between devices and neglect the contextual presentation of service reviews. This study aims to address these gaps by exploring the contextual representation of each device-service pair. Firstly, we propose a latent features combination technique that can capture latent feature interactions, by aggregating the device-device relationships within the SIoT. Then, we leverage Factorization Machines to model higher-order feature interactions specific to each SIoT device-service pair to accomplish accurate rating prediction. Finally, we propose a service recommendation framework for SIoT based on review aggregation and feature learning processes. The experimental evaluation demonstrates the framework's effectiveness in improving service recommendation accuracy and relevance.

Related papers

• Adopting Large Language Models to Automated System Integration [0.0]
  We introduce a software architecture for automated service composition using Large Language Models (LLMs) We propose a novel natural language query-based benchmark for service discovery. We extend the benchmark to complete service composition scenarios.
  arXiv  Detail & Related papers  (2025-04-11T12:42:01Z)
• Leveraging LLMs to Create a Haptic Devices' Recommendation System [9.262120813656553]
  This paper uses Large Language Models to develop a haptic agent for haptic device recommendation. The proposed haptic recommendation agent ranks in the top 10% across all UEQ categories with mean differences favoring the agent in nearly all subscales.
  arXiv  Detail & Related papers  (2025-01-22T01:41:05Z)
• A Survey on Intent-aware Recommender Systems [8.761638205244427]
  A recommender system should aim to take the users' probable intent of using the service at a certain point in time into account. In this paper, we survey and categorize existing approaches to building the next generation of Intent-Aware Recommender Systems.
  arXiv  Detail & Related papers  (2024-06-24T06:46:32Z)
• Semantic-Enhanced Relational Metric Learning for Recommender Systems [27.330164862413184]
  Recently, metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. We propose a joint Semantic-Enhanced Metric Learning framework to tackle the problem in recommender systems. Specifically the semantic signal is first extracted from the target reviews containing abundant features and personalized user preferences. A novel regression model is then designed via leveraging the extracted semantic signal to improve the discriminative ability of original relation-based training process.
  arXiv  Detail & Related papers  (2024-06-07T11:54:50Z)
• CELA: Cost-Efficient Language Model Alignment for CTR Prediction [70.65910069412944]
  Click-Through Rate (CTR) prediction holds a paramount position in recommender systems.<n>Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs)<n>We propose textbfCost-textbfEfficient textbfLanguage Model textbfAlignment (textbfCELA) for CTR prediction.
  arXiv  Detail & Related papers  (2024-05-17T07:43:25Z)
• Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems. This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation [56.98380787425388]
  We propose a search-Enhanced framework for the Sequential Recommendation (SESRec) SESRec disentangling similar and dissimilar representations within S&R behaviors. Experiments on both industrial and public datasets demonstrate that SESRec consistently outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-18T09:04:50Z)
• TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features [0.5161531917413706]
  Temporal Prediction is essential to identify a suitable service over time. Recent methods hardly achieved desired accuracy due to various limitations. This paper proposes a scalable strategy for Temporal Prediction using Multi-source Collaborative-Features.
  arXiv  Detail & Related papers  (2023-03-30T06:49:53Z)
• Improving Recommendation Relevance by simulating User Interest [77.34726150561087]
  We observe that recommendation "recency" can be straightforwardly and transparently maintained by iterative reduction of ranks of inactive items. The basic idea behind this work is patented in a context of online recommendation systems.
  arXiv  Detail & Related papers  (2023-02-03T03:35:28Z)
• Recent Advances in Heterogeneous Relation Learning for Recommendation [5.390295867837705]
  We review the development of recommendation frameworks with the focus on heterogeneous relational learning. The objective of this task is to map heterogeneous relational data into latent representation space. We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks.
  arXiv  Detail & Related papers  (2021-10-07T13:32:04Z)
• An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration [55.67505546330206]
  We give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques. In this paper, we give examples of the application of recommender systems and machine learning and discuss future research issues.
  arXiv  Detail & Related papers  (2021-02-12T17:21:36Z)
• Evaluating Interactive Summarization: an Expansion-Based Framework [97.0077722128397]
  We develop an end-to-end evaluation framework for interactive summarization. Our framework includes a procedure of collecting real user sessions and evaluation measures relying on standards. All of our solutions are intended to be released publicly as a benchmark.
  arXiv  Detail & Related papers  (2020-09-17T15:48:13Z)
• Recommender Systems for the Internet of Things: A Survey [53.865011795953706]
  Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things. Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data.
  arXiv  Detail & Related papers  (2020-07-14T01:24:44Z)
• Service mining for Internet of Things [1.6371451481715193]
  A service mining framework is proposed that enables discovering interesting relationships in Internet of Things services bottom-up. We present a set of metrics to evaluate the interestingness of discovered service relationships.
  arXiv  Detail & Related papers  (2020-05-14T11:58:34Z)
• A Bayesian Approach to Conversational Recommendation Systems [60.12942570608859]
  We present a conversational recommendation system based on a Bayesian approach. A case study based on the application of this approach to emphstagend.com, an online platform for booking entertainers, is discussed.
  arXiv  Detail & Related papers  (2020-02-12T15:59:31Z)

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.