Unravelling the dynamics of online ratings

Online product ratings are an immensely important source of information for consumers and accordingly a strong driver of commerce. Nonetheless, interpreting a particular rating in context can be very challenging. Ratings show significant variation over time, so understanding the reasons behind that variation is important for consumers, platform designers, and product creators. In this paper we contribute a set of tools and results that help shed light on the complexity of ratings dynamics. We consider multiple item types across multiple ratings platforms, and use a interpretable model to decompose ratings in a manner that facilitates comprehensibility. We show that the various kinds of dynamics observed in online ratings are largely understandable as a product of the nature of the ratings platform, the characteristics of the user population, known trends in ratings behavior, and the influence of recommendation systems. Taken together, these results provide a framework for both quantifying and interpreting the factors that drive the dynamics of online ratings.Published versio

Consumer Acceptance of Recommendations by Interactive Decision Aids: The Joint Role of Temporal Distance and Concrete vs. Abstract Communications

Interactive decision aids (IDAs) typically use concrete product feature-based approaches to interact with consumers. Recently however, interaction designs that focus on communicating abstract consumer needs have been suggested as a promising alternative. This article investigates how temporal distance moderates the effectiveness of these two competing IDA communication designs by its effect on consumers’ mental representation of the product decision problem. Temporal distance is inherently connected to IDAs in two ways. Congruency between consumption timing (immediate vs. distant) and IDA communication design (concrete vs. abstract, respectively) increases the likelihood to accept the IDA’s advice. This effect is also achieved by congruency between IDA process timing (immediate vs. delayed delivery of recommendations) and IDA communication design (concrete vs. abstract, respectively). We further show that this process is mediated by the perceived transparency of the IDA process. Managers and researchers need to take into account the importance of congruency between the user and the interface through which companies interact with their users and can further optimize IDAs so that they better match consumers’ mental representations

Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items

In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively