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Adaptive collaborative filtering

Published: 23 October 2008 Publication History

Abstract

We present a flexible approach to collaborative filtering which stems from basic research results. The approach is flexible in several dimensions: We introduce an algorithm where the loss can be tailored to a particular recommender problem. This allows us to optimize the prediction quality in a way that matters for the specific recommender system. The introduced algorithm can deal with structured estimation of the predictions for one user. The most prominent outcome of this is the ability of learning to rank items along user preferences. To this end, we also present a novel algorithm to compute the ordinal loss in O(n log(n)) as apposed to O(n2). We extend this basic model such that it can accommodate user and item offsets as well as user and item features if they are present. The latter unifies collaborative filtering with content based filtering. We present an analysis of the algorithm which shows desirable properties in terms of privacy needs of users, parallelization of the algorithm as well as collaborative filtering as a service. We evaluate the algorithm on data provided by WikiLens. This data is a cross-domain data set as it contains ratings on items from a vast array of categories. Evaluation shows that cross-domain prediction is possible.

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  • (2024)A Knowledge Sharing Approach to Foster Interdisciplinary Pedestrian Dynamics and Epidemiological Modeling Research and PracticeProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-3556-3_25(313-324)Online publication date: 10-Aug-2024
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cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2008

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Author Tags

  1. collaborative filtering
  2. structured estimation

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RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2024)Safe Collaborative FilteringSSRN Electronic Journal10.2139/ssrn.4767721Online publication date: 2024
  • (2024)KGCNA: Knowledge Graph Collaborative Neighbor Awareness Network for RecommendationIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33699768:4(2736-2748)Online publication date: Aug-2024
  • (2024)A Knowledge Sharing Approach to Foster Interdisciplinary Pedestrian Dynamics and Epidemiological Modeling Research and PracticeProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-3556-3_25(313-324)Online publication date: 10-Aug-2024
  • (2024)Multi-relational Heterogeneous Graph Attention Networks for Knowledge-Aware RecommendationWeb and Big Data10.1007/978-981-97-2421-5_8(108-123)Online publication date: 12-May-2024
  • (2023)Movie Ticket, Popcorn, and Another Movie Next Weekend: Time-Aware Service Sequential Recommendation for User RetentionCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3584628(361-365)Online publication date: 30-Apr-2023
  • (2023)Knowledge-Adaptive Contrastive Learning for RecommendationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570483(535-543)Online publication date: 27-Feb-2023
  • (2023)PKAT: Pre-training in Collaborative Knowledge Graph Attention Network for Recommendation2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00054(448-457)Online publication date: 1-Dec-2023
  • (2022)Recommendations with residual connections and negative sampling based on knowledge graphsKnowledge-Based Systems10.1016/j.knosys.2022.110049258(110049)Online publication date: Dec-2022
  • (2022)Reduce unrelated Knowledge through Attribute Collaborative signal for knowledge graph recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117078201:COnline publication date: 1-Sep-2022
  • (2020)A Hierarchical Knowledge and Interest Propagation Network for Recommender Systems2020 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW51313.2020.00026(119-126)Online publication date: Nov-2020
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