PsycHin: Finding the Trade-Off With Heterogeneous Information Network Toward Psychometric Preference Assessment

In psychometry, psychometric profiles of an individual subject as well as correlations among the psychometric profiles are crucial supports for assessing the psychometric preference of the individual subject. Intuitively, while sufficient psychometric profiles in terms of amount and types yield high assessment accuracy, too many psychometric profiles increase workloads of subjects, because measuring a psychometric profile of an individual subject requires filling out one psychological questionnaire at least. The trade-off between assessment accuracy and participant burden constitutes a core challenge in psychometric research, because it directly affects both the statistical validity of findings and the cognitive feasibility of large-scale evaluation. Existing assessment frameworks often struggle to reconcile efficiency with precision. This challenge underscores the need for a new modeling paradigm capable of reducing measurement redundancy while preserving interpretability and reliability. Therefore, inspired by the semantic field, this paper defines a new concept, namely profile field, and proposes a novel framework, i.e., PsycHin, to achieve the trade-off. Specifically, PsycHin can be decomposed into three phases: $i$) modeling the psychologically cognitive processes, where PsycHin learns multiple explicit and implicit correlations among psychometric profiles based on a heterogeneous information network and defines a profile meta structure to guide instance extraction; $ii$) fitting data and calculating similarities among subjects, where PsycHin utilizes tanh to make up for the rating biases caused by the discrete scores from options of psychological questionnaires and employs entropy to measure the cognitive levels of subjects on same objects; and $iii$) constructing profile fields, where PsycHin calculates the importance of psychometric profiles and filters the noisy ones by leveraging the contribution rates of psychometric profiles. Our experimental results not only show that PsycHin performs well, but also demonstrate that incorporating the heterogeneous information network into psychometric preference assessment does improve the performance of PsycHin.