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Unifying nearest neighbors collaborative filtering

Published: 06 October 2014 Publication History

Abstract

We study collaborative filtering for applications in which there exists for every user a set of items about which the user has given binary, positive-only feedback (one-class collaborative filtering). Take for example an on-line store that knows all past purchases of every customer. An important class of algorithms for one-class collaborative filtering are the nearest neighbors algorithms, typically divided into user-based and item-based algorithms. We introduce a reformulation that unifies user- and item-based nearest neighbors algorithms and use this reformulation to propose a novel algorithm that incorporates the best of both worlds and outperforms state-of-the-art algorithms. Additionally, we propose a method for naturally explaining the recommendations made by our algorithm and show that this method is also applicable to existing user-based nearest neighbors methods.

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Cited By

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  • (2024)Neighborhood-Based Collaborative Filtering for Conversational RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688191(1045-1050)Online publication date: 8-Oct-2024
  • (2024)On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671687(1222-1233)Online publication date: 25-Aug-2024
  • (2022)On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering DatasetsInformation10.3390/info1309042813:9(428)Online publication date: 11-Sep-2022
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cover image ACM Conferences
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems
October 2014
458 pages
ISBN:9781450326681
DOI:10.1145/2645710
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: 06 October 2014

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Author Tags

  1. explaining recommendations
  2. nearest neighbours
  3. one-class collaborative filtering
  4. recommender systems
  5. top-n recommendation

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RecSys'14
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RecSys'14: Eighth ACM Conference on Recommender Systems
October 6 - 10, 2014
California, Foster City, Silicon Valley, USA

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Overall Acceptance Rate 85 of 414 submissions, 21%

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Cited By

View all
  • (2024)Neighborhood-Based Collaborative Filtering for Conversational RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688191(1045-1050)Online publication date: 8-Oct-2024
  • (2024)On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671687(1222-1233)Online publication date: 25-Aug-2024
  • (2022)On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering DatasetsInformation10.3390/info1309042813:9(428)Online publication date: 11-Sep-2022
  • (2022)On Producing Accurate Rating Predictions in Sparse Collaborative Filtering DatasetsInformation10.3390/info1306030213:6(302)Online publication date: 15-Jun-2022
  • (2022)Weighted Similarity and Core-User-Core-Item Based RecommendationsEntropy10.3390/e2405060924:5(609)Online publication date: 27-Apr-2022
  • (2022)RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback DataProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551472(648-651)Online publication date: 12-Sep-2022
  • (2022)Rethinking Correlation-based Item-Item Similarities for Recommender SystemsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532055(2287-2291)Online publication date: 6-Jul-2022
  • (2022)Sequential recommendationInformation Sciences: an International Journal10.1016/j.ins.2022.07.079609:C(660-678)Online publication date: 1-Sep-2022
  • (2022)A probabilistic perspective on nearest neighbor for implicit recommendationInternational Journal of Data Science and Analytics10.1007/s41060-022-00367-416:2(217-235)Online publication date: 29-Oct-2022
  • (2022)A recommender system based on collaborative filtering, graph theory using HMM based similarity measuresInternational Journal of System Assurance Engineering and Management10.1007/s13198-021-01537-613:S1(533-545)Online publication date: 4-Jan-2022
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