English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users’ fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to address the cold-start problem. However, some of the users’ preferences collected are usually not complete due to users’ cognitive limitation and/or unfamiliarity on item domains, which is called ‘partial preferences’. The fundamental challenge and then negatively affects to accurately recommend items according to users’ preferences through MCRSs. In this paper, we propose a Hypothetical Tensor Model (HTM) to leverage auxiliary data complemented through three intuitive rules dealing with user’s unfamiliarity. First, we find four patterns of partial preferences that are caused by users’ unfamiliarity. And then the rules are defined by considering relationships between multi-criteria. Lastly, complemented preferences are modeled by a tensor to maintain an inherent structure of and correlations between the multi-criteria. Experiments on a TripAdvisor dataset showed that HTM improves MSE performances from 40 to 47% by comparing with other baseline methods. In particular, effectivenesses of each rule regarding multi-criteria on HTM are clearly revealed.

Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences