Reputation vs. price: Sequential recommendations based on cue diagnosticity theory

Sequential recommendations have been widely used in e-commerce platforms to effectively capture consumers' dynamic preferences and provide them with preferred products. Traditional models usually use ratings and product attributes for sequential recommendations to satisfy consumers’ more personalized needs. Consumers also rely on reviews from other consumers to form a general impression of the product or retailer before making their purchase decisions. Such impressions can be treated as reputations of the product or retailer. Inspired by cue diagnosticity theory, we divide the attributes related to product purchase into low- and high-scope cues. High-scope cues, including reputations, are not easily changed because they are formed over a long period by numerous consumers, whereas low-scope cues, such as price, can be easily changed by retailers. We propose an innovative Sequential Recommendation model by Integrating Low-scope cues and High-scope cues (SRILH). We design a cue-extraction layer to extract high-scope cues from consumer online reviews and a hierarchical cue-aware attention layer to learn the joint effect of low- and high-scope cues. We evaluate the performance of the proposed model using three real-world datasets, and our experimental results validate its effectiveness and robustness. Our research contributes to sequential recommendations research by uncovering the joint effects of cues on consumer behavior and by providing valuable insights into the dynamics of cue preference formation in recommendation systems. We also extend the empirical literature on cue diagnosticity theory by drawing conclusions from the micro and individual perspectives to shed light on how different cues impact consumer choices. The interpretable visualization results provide managerial insights for retailers and manufacturers to improve their products.