Article

Naïve filterbots for robust cold-start recommendations

Authors:

Seung-Taek Park,

Dennis DeCosteAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 699 - 705

https://doi.org/10.1145/1150402.1150490

Published: 20 August 2006 Publication History

Abstract

The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any other recommendation method. However, in cold-start situations - where a user, an item, or the entire system is new - simple non-personalized recommendations often fare better. We improve the scalability and performance of a previous approach to handling cold-start situations that uses filterbots, or surrogate users that rate items based only on user or item attributes. We show that introducing a very small number of simple filterbots helps make CF algorithms more robust. In particular, adding just seven global filterbots improves both user-based and item-based CF in cold-start user, cold-start item, and cold-start system settings. Performance is better when data is scarce, performance is no worse when data is plentiful, and algorithm efficiency is negligibly affected. We systematically compare a non-personalized baseline, user-based CF, item-based CF, and our bot-augmented user- and item-based CF algorithms using three data sets (Yahoo! Movies, MovieLens, and EachMovie) with the normalized MAE metric in three types of cold-start situations. The advantage of our "naïve filterbot" approach is most pronounced for the Yahoo! data, the sparsest of the three data sets.

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Index Terms

Naïve filterbots for robust cold-start recommendations
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Copyright © 2006 ACM.

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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Published: 20 August 2006

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Gu JFu YHung K(2024)On Intelligent Placement Decision-Making Algorithms for Wireless Digital Twin Networks via Bandit LearningIEEE Transactions on Vehicular Technology10.1109/TVT.2024.336095973:6(8889-8902)Online publication date: Jun-2024
https://doi.org/10.1109/TVT.2024.3360959
Liu TGao CWang ZLi DHao JJin DLi YChen HDuh WHuang HKato MMothe JPoblete B(2023)Uncertainty-aware Consistency Learning for Cold-Start Item RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592078(2466-2470)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592078
Zhao PLuo ALiu YXu JLi ZZhuang FSheng VZhou X(2022)Where to Go Next: A Spatio-Temporal Gated Network for Next POI RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.300719434:5(2512-2524)Online publication date: 1-May-2022
https://doi.org/10.1109/TKDE.2020.3007194
Joy JRaj NV. G. R(2021)Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start ProblemJournal of Data and Information Quality10.1145/342925113:3(1-27)Online publication date: 27-Apr-2021
https://dl.acm.org/doi/10.1145/3429251
Song CDong H(2021)Application of Intelligent Recommendation for Agricultural Information: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2021.31272019(153616-153632)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3127201
Mishra NChaturvedi SVij ATripathi S(2021)Research Problems in Recommender systemsJournal of Physics: Conference Series10.1088/1742-6596/1717/1/0120021717(012002)Online publication date: 10-Jan-2021
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Jeevamol JRenumol V(2021)An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problemEducation and Information Technologies10.1007/s10639-021-10508-0Online publication date: 30-Mar-2021
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Manju G. Abhinaya P. Hemalatha M.R. Manju Ganesh G. Manju G.G. (2020)Cold Start Problem Alleviation in a Research Paper Recommendation System Using the Random Walk Approach on a Heterogeneous User-Paper GraphInternational Journal of Intelligent Information Technologies10.4018/IJIIT.202004010216:2(24-48)Online publication date: Apr-2020
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Mohammadi MNaree SLati M(2020)User-item content awareness in matrix factorization based collaborative recommender systemsIntelligent Data Analysis10.3233/IDA-19459924:3(723-739)Online publication date: 21-May-2020
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