Improving co-SVD for cold-start recommendations using sparsity reduction

Low Jia Ming, Chern Hong Lim, Ian K.T. Tan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Recommender systems are highly dependent on the users' or items' historical data. The completeness of the data determines the performance of the models, especially for models based on the collaborative filtering (CF) technique. Under cold-start situations, where there are limited relevant historical data on the users' or items' information, producing accurate recommendations are challenging. We propose the use of the implicit Alternating Least Square (iALS) method to predict users' preferences and impute it into the matrix co-factorization algorithm, co-SVD. The proposed approach aims to alleviate the cold-start problem that is most evident in large datasets that have high sparsity. In addition, we included the results for two cold-start situations, cold-start user and cold-start item (long-tail), using our hybrid co-SVD with artificial ratings imputation. The F1 score of the top-5 recommendations generated by the proposed approach improved from 25.08% to 30.09% under the cold-start user situation. With the long-tail item situation, the proposed approach improved from 20.8% to 23.19%. The proposed approach is method-agnostic, and other CF-based models can benefit from this imputation method.

Original languageEnglish
Title of host publicationProceedings of 2022 APSIPA - Annual Summit and Conference Chiang Mai, Thailand November 7-10, 2022
EditorsUkrit Mankong
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages990-996
Number of pages7
ISBN (Electronic)9786165904773
ISBN (Print)9781665486620
DOIs
Publication statusPublished - 2022
EventAnnual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA) 2022 - Chiang Mai, Thailand
Duration: 7 Nov 202210 Nov 2022
https://ieeexplore.ieee.org/xpl/conhome/9979726/proceeding (Proceedings)
https://www.apsipa2022.org (Website)

Conference

ConferenceAnnual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA) 2022
Abbreviated titleAPSIPA ASC 2022
Country/TerritoryThailand
CityChiang Mai
Period7/11/2210/11/22
Internet address

Keywords

  • cold-start problem
  • implicit feedback
  • matrix co-factorization
  • Recommender system

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