TY - JOUR
T1 - Comprehensive helpfulness of online reviews
T2 - A dynamic strategy for ranking reviews by intrinsic and extrinsic helpfulness
AU - Qin, Jindong
AU - Zheng, Pan
AU - Wang, Xiaojun
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Information overload often makes it difficult for consumers to identify valuable online reviews through the traditional “helpful votes” button in the big data era, so it is essential to locate helpful reviews. Unlike the existing efforts that often measure online reviews’ helpfulness one-sidedly, this study takes the intrinsic helpfulness (IH) and extrinsic helpfulness (EH) into account, and the intrinsic-extrinsic comprehensive helpfulness (ICH-ECH) plot can be constructed by ensemble neural network model (ENNM) and time-weighted standard deviation accordingly. Furthermore, this study proposes a measure of EH ignored by previous studies, that is, the percentage of negative replies, which contain useful information that can measure online reviews helpfulness. We corrected it with a time sliding window by an improved iterative Bayesian probability approach (IBPA). In addition, this study further proposes a dynamic time-aware helpfulness ranking (DTAHR) model to dynamically rank reviews and identify beneficial reviews in a short time. We used real data sets from JD.com to conduct all experiments. The experimental results show that the performance of the DTAHR model is significantly better than other strategies. Our findings offer guidelines to evaluate the helpfulness of online reviews from multiple perspectives and rank them dynamically.
AB - Information overload often makes it difficult for consumers to identify valuable online reviews through the traditional “helpful votes” button in the big data era, so it is essential to locate helpful reviews. Unlike the existing efforts that often measure online reviews’ helpfulness one-sidedly, this study takes the intrinsic helpfulness (IH) and extrinsic helpfulness (EH) into account, and the intrinsic-extrinsic comprehensive helpfulness (ICH-ECH) plot can be constructed by ensemble neural network model (ENNM) and time-weighted standard deviation accordingly. Furthermore, this study proposes a measure of EH ignored by previous studies, that is, the percentage of negative replies, which contain useful information that can measure online reviews helpfulness. We corrected it with a time sliding window by an improved iterative Bayesian probability approach (IBPA). In addition, this study further proposes a dynamic time-aware helpfulness ranking (DTAHR) model to dynamically rank reviews and identify beneficial reviews in a short time. We used real data sets from JD.com to conduct all experiments. The experimental results show that the performance of the DTAHR model is significantly better than other strategies. Our findings offer guidelines to evaluate the helpfulness of online reviews from multiple perspectives and rank them dynamically.
U2 - 10.1016/j.dss.2022.113859
DO - 10.1016/j.dss.2022.113859
M3 - Article (Academic Journal)
SN - 0167-9236
VL - 163
JO - Decision Support Systems
JF - Decision Support Systems
M1 - 113859
ER -