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User preference learning in multi-criteria recommendations using stacked auto encoders

Published: 27 September 2018 Publication History

Abstract

Recommender System (RS) is an essential component of many businesses, especially in e-commerce domain. RS exploits the preference history (rating, purchase, review, etc.) of users in order to provide the recommendations. A user in traditional RS can provide only one rating value about an item. Deep Neural Networks have been used in this single rating system to improve recommendation accuracy in the recent times. However, the single rating systems are inadequate to understand the usersfi preferences about an item. On the other hand, business enterprises such as tourism, e-learning, etc. facilitate users to provide multiple criteria ratings about an item, thus it becomes easier to understand users' preference over single rating system. In this paper, we propose an extended Stacked Autoencoders (a Deep Neural Network technique) to utilize the multi-criteria ratings. The proposed network is designed to learn the relationship between each user's criteria and overall rating efficiently. Experimental results on real world datasets (Yahoo! Movies and TripAdvisor) demonstrate that the proposed approach outperforms state-of-the-art single rating systems and multi-criteria approaches on various performance metrics.

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  • (2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
  • (2024)An Approach for Multi-Context-Aware Multi-Criteria Recommender Systems Based on Deep LearningIEEE Access10.1109/ACCESS.2024.342863012(99936-99948)Online publication date: 2024
  • (2024)Deep ensembled multi-criteria recommendation system for enhancing and personalizing the user experience on e-commerce platformsKnowledge and Information Systems10.1007/s10115-024-02187-366:12(7799-7836)Online publication date: 1-Dec-2024
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    cover image ACM Conferences
    RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
    September 2018
    600 pages
    ISBN:9781450359016
    DOI:10.1145/3240323
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 27 September 2018

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    Author Tags

    1. collaborative filtering
    2. deep learning
    3. multi-criteria ratings
    4. stacked autoencoders

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    • Short-paper

    Funding Sources

    • Media Lab Asia
    • Govt. of India
    • Ministry of Electronics and Information Technology (MeitY)

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    RecSys '18
    Sponsor:
    RecSys '18: Twelfth ACM Conference on Recommender Systems
    October 2, 2018
    British Columbia, Vancouver, Canada

    Acceptance Rates

    RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

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    • (2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
    • (2024)An Approach for Multi-Context-Aware Multi-Criteria Recommender Systems Based on Deep LearningIEEE Access10.1109/ACCESS.2024.342863012(99936-99948)Online publication date: 2024
    • (2024)Deep ensembled multi-criteria recommendation system for enhancing and personalizing the user experience on e-commerce platformsKnowledge and Information Systems10.1007/s10115-024-02187-366:12(7799-7836)Online publication date: 1-Dec-2024
    • (2023)Using Graph Neural Networks for Social RecommendationsAlgorithms10.3390/a1611051516:11(515)Online publication date: 10-Nov-2023
    • (2023)Multi-Attribute BERT for Preferences Completion in Multi-Criteria Recommender SystemProceedings of the 2023 12th International Conference on Software and Computer Applications10.1145/3587828.3587875(315-320)Online publication date: 23-Feb-2023
    • (2023)Recommendation System for Human Resource Management by the Use of Apache Spark Cluster2023 17th International Conference on Electronics Computer and Computation (ICECCO)10.1109/ICECCO58239.2023.10147129(1-4)Online publication date: 1-Jun-2023
    • (2023)To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative FilteringIEEE Access10.1109/ACCESS.2023.327026011(41979-41994)Online publication date: 2023
    • (2023)Integrating Machine Learning and Evidential Reasoning for User Profiling and RecommendationJournal of Systems Science and Systems Engineering10.1007/s11518-023-5569-532:4(393-412)Online publication date: 13-Jun-2023
    • (2023)Deep encoder–decoder-based shared learning for multi-criteria recommendation systemsNeural Computing and Applications10.1007/s00521-023-09007-935:34(24347-24356)Online publication date: 30-Sep-2023
    • (2022)Application of Hybrid Filtering Strategies in Music Recommendation SystemJournal of Ubiquitous Computing and Communication Technologies10.36548/jucct.2022.3.0044:3(159-169)Online publication date: 15-Sep-2022
    • Show More Cited By

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