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Boosting Recommender Systems with Advanced Embedding Models

Published: 20 April 2020 Publication History

Abstract

Recommender systems are paramount in providing personalized content and intelligent content filtering on any social media platform, web portal, and online application. In line with the current trends in the field directed towards mapping problem and data encoding representations from other fields, this research investigates the feasibility of augmenting a graph-based recommender system for Amazon products with two state-of-the-art representation models. In particular, the potential benefits of using the language representation model BERT and GraphSage based representations of nodes and edges for improving the quality of the recommendations were investigated. Link prediction and link attribute inference were used to identify the products that the users will buy and predict the rating they will give to a product, respectively. The initial results of our exploratory study are encouraging and point to potential directions for future research.

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Christos Sardianos, Grigorios Ballas Papadatos, and Iraklis Varlamis. 2019. Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance. Information. 10. 155. (2019)
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Cited By

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  • (2024)Data-driven drug treatment: enhancing clinical decision-making with SalpPSO-optimized GraphSAGEComputer Methods in Biomechanics and Biomedical Engineering10.1080/10255842.2024.2399012(1-23)Online publication date: 18-Sep-2024
  • (2024)Computing recommendations from free-form textExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121268236:COnline publication date: 1-Feb-2024
  • (2023)Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware RecommendationsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592965(1-12)Online publication date: 18-Jun-2023
  • Show More Cited By

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            cover image ACM Conferences
            WWW '20: Companion Proceedings of the Web Conference 2020
            April 2020
            854 pages
            ISBN:9781450370240
            DOI:10.1145/3366424
            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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            New York, NY, United States

            Publication History

            Published: 20 April 2020

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            Author Tags

            1. graph embeddings
            2. link prediction
            3. recommender systems
            4. word embeddings

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            WWW '20
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            WWW '20: The Web Conference 2020
            April 20 - 24, 2020
            Taipei, Taiwan

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            Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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            Cited By

            View all
            • (2024)Data-driven drug treatment: enhancing clinical decision-making with SalpPSO-optimized GraphSAGEComputer Methods in Biomechanics and Biomedical Engineering10.1080/10255842.2024.2399012(1-23)Online publication date: 18-Sep-2024
            • (2024)Computing recommendations from free-form textExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121268236:COnline publication date: 1-Feb-2024
            • (2023)Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware RecommendationsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592965(1-12)Online publication date: 18-Jun-2023
            • (2022)Privacy-aware network embedding-based ensemble for social recommendationThe Journal of Supercomputing10.1007/s11227-022-04958-779:8(8912-8939)Online publication date: 27-Dec-2022
            • (2021)Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word RepresentationsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474272(187-198)Online publication date: 13-Sep-2021
            • (2012)Semantics and Content-Based RecommendationsRecommender Systems Handbook10.1007/978-1-0716-2197-4_7(251-298)Online publication date: 24-Feb-2012

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