research-article

Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information

Authors:

Nima Mirbakhsh,

Charles X. LingAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 9, Issue 4

Article No.: 33, Pages 1 - 19

https://doi.org/10.1145/2724720

Published: 01 June 2015 Publication History

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Abstract

Making accurate recommendations for cold-start users is a challenging yet important problem in recommendation systems. Including more information from other domains is a natural solution to improve the recommendations. However, most previous work in cross-domain recommendations has focused on improving prediction accuracy with several severe limitations. In this article, we extend our previous work on clustering-based matrix factorization in single domains into cross domains. In addition, we utilize recent results on unobserved ratings. Our new method can more effectively utilize data from auxiliary domains to achieve better recommendations, especially for cold-start users. For example, our method improves the recall to 21% on average for cold-start users, whereas previous methods result in only 15% recall in the cross-domain Amazon dataset. We also observe almost the same improvements in the Epinions dataset. Considering that it is often difficult to make even a small improvement in recommendations, for cold- start users in particular, our result is quite significant.

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Index Terms

Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information
1. Information systems
  1. Information retrieval
  2. Information storage systems

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Reviews

Reviewer: A. Squassabia

Collaborative recommender systems often provide disappointing suggestions to new users who volunteered very few or no ratings of their own for processing: this is known as the cold-start problem. Mitigating such shortcomings with cross-domain information helps; this paper builds on previous work by the authors to introduce incremental improvements to the cold-start problem. The authors previously published an approach to single-domain recommendations based on clustering of latent factors using matrix factorization and k -means. Here, they translate the same principle into seeding cold-start recommendations with cross-domain information, exploiting multiple domains where each domain is endowed with shared users, hence with some measure of user overlap. Validation was carried out using two datasets, one from Amazon comprising ratings for media (video, music, DVD) and goods (electronics, kitchen, toys) and another from Epinions comprising ten disparate categories of items. Validation compared results for single-domain, traditional cross-domain, and their new clustered cross-domain top- N ratings using recall for N of 5, 10, 15 or 20 as a metric. Cross-domain cold-start performed better than single-domain; clustered cross-domain performed as well as traditional cross-domain for low N , and better than traditional for larger N . The main contribution of this paper is the novelty of a relatively simple implementation for the underlying idea, which is not entirely original but new in this form. Its main limitation is in the difficulty of assessing impact on the basis of a single machine-driven metric on only two datasets; albeit traditionally acceptable, validation would be more informative if carried out with more data, multiple metrics, and ideally before a live audience. Online Computing Reviews Service

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Information & Contributors

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 9, Issue 4

June 2015

261 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2786971

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

Issue’s Table of Contents

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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Association for Computing Machinery

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Publication History

Published: 01 June 2015

Accepted: 01 January 2015

Revised: 01 December 2014

Received: 01 June 2014

Published in TKDD Volume 9, Issue 4

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https://doi.org/10.3390/electronics13081547
Liu JShang LSu YNie WWen XLiu A(2024)Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge GraphACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363970620:5(1-18)Online publication date: 5-Jan-2024
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Liu XFu XDe Meo PFiumara G(2024)Cross-Domain Recommendation To Cold-Start Users Via Categorized Preference TransferThe Computer Journal10.1093/comjnl/bxae02967:8(2610-2621)Online publication date: 6-Apr-2024
https://doi.org/10.1093/comjnl/bxae029
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Li HYu LNiu XLeng YDu Q(2023)Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer GateACM Transactions on Knowledge Discovery from Data10.1145/360461518:1(1-28)Online publication date: 10-Aug-2023
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Recommendations

Naïve filterbots for robust cold-start recommendations

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