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An Analysis on Time- and Session-aware Diversification in Recommender Systems

Published: 09 July 2017 Publication History

Abstract

In modern recommender systems, diversity has been widely acknowledged as an important factor to improve user experience and, more recently, intent-aware approaches to diversification have been proposed to provide the user with a list of recommendations covering different aspects of her behavior. In this paper, we propose and analyze the performances of two diversification methods taking into account temporal aspects of the user profile: in the first one we adopt a temporal decay function to emphasize the importance of more recent items in the user profile while in the second one we perform an evaluation based on the identification and analysis of temporal sessions. The two proposed methods have been implemented as temporal variants of the well-known xQuAD framework. In both cases, experimental results on Netflix 100M show an improvement in terms of accuracy-diversity balance.

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Cited By

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  • (2023)Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation SystemsFuture Internet10.3390/fi1510032315:10(323)Online publication date: 28-Sep-2023
  • (2022)Relevancy or Diversity?Journal of Global Information Management10.4018/JGIM.31092930:1(1-24)Online publication date: 10-Aug-2022
  • (2022)Personalized Graph Neural Networks With Attention Mechanism for Session-Aware RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303132934:8(3946-3957)Online publication date: 1-Aug-2022
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Published In

cover image ACM Conferences
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
July 2017
420 pages
ISBN:9781450346351
DOI:10.1145/3079628
  • General Chairs:
  • Maria Bielikova,
  • Eelco Herder,
  • Program Chairs:
  • Federica Cena,
  • Michel Desmarais
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: 09 July 2017

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Author Tags

  1. diversity
  2. novelty
  3. recommendation
  4. recommender systems
  5. reranking
  6. session
  7. time
  8. xquad

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UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

View all
  • (2023)Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation SystemsFuture Internet10.3390/fi1510032315:10(323)Online publication date: 28-Sep-2023
  • (2022)Relevancy or Diversity?Journal of Global Information Management10.4018/JGIM.31092930:1(1-24)Online publication date: 10-Aug-2022
  • (2022)Personalized Graph Neural Networks With Attention Mechanism for Session-Aware RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303132934:8(3946-3957)Online publication date: 1-Aug-2022
  • (2022)Semantic Interpretation of Top-N RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.301021534:5(2416-2428)Online publication date: 1-May-2022
  • (2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
  • (2022)Rank-sensitive proportional aggregations in dynamic recommendation scenariosUser Modeling and User-Adapted Interaction10.1007/s11257-021-09311-w32:4(685-746)Online publication date: 1-Jan-2022
  • (2021)Prioritized multi-criteria federated learningIntelligenza Artificiale10.3233/IA-20005414:2(183-200)Online publication date: 11-Jan-2021
  • (2021)Third Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Proceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3470933(806-809)Online publication date: 13-Sep-2021
  • (2021)Personalizing Diversity Versus Accuracy in Session-Based Recommender SystemsSN Computer Science10.1007/s42979-020-00399-22:1Online publication date: 15-Jan-2021
  • (2021)A flexible framework for evaluating user and item fairness in recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-020-09285-1Online publication date: 27-Jan-2021
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