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Online shopping generates billions of dollars in revenues, including both the physical goods and online services. Product images and associated descriptions are the two main sources of information used by the shoppers to gain knowledge about a product. However, these two pieces of information may not always present the true picture of the product. Images could be deceiving, and descriptions could be overwhelming or cryptic. Moreover, the relative rank of these products among the peers may lead to inconsistencies. Hence, a useful and widely used piece of information is “user reviews”. A number of vendors like Amazon have created whole ecosystems around user reviews, thereby boosting their revenues. However, extracting the relevant and useful information out of the plethora of reviews is not straight forward, and is a very tedious job. In this paper we propose a product ranking system that facilitates the online shopping experience by analyzing the reviews for sentiments, evaluating their usefulness, extracting and weighing different product features and aspects, ranking it among similar comparable products, and finally creating a unified rank for each product. Experiment results show the usefulness of our proposed approach in providing an effective and reliable online shopping experience in comparison with similar approaches.

Online search intermediaries, such as Amazon or Expedia, use rankings (ordered lists) to present third-party sellers’ products to consumers. These rankings decrease consumer search costs and increase the probability of a match with a seller, ultimately increasing consumer welfare. Constructing relevant rankings requires understanding their causal effect on consumer choices. However, this is challenging because rankings are endogenous: consumers pay more attention to highly ranked products, and intermediaries rank the most relevant products at the top. In this paper, I use the first data set with experimental variation in the ranking from a field experiment at Expedia to make three contributions. First, I identify the causal effect of rankings and show that they affect what consumers search, but conditional on search, do not affect purchases. Second, I quantify the effect of rankings using a sequential search model and find an average position effect of $1.92, which is lower than literature estimates obtained without experimental variation. I also use model predictions, data patterns, and a feature of the data set (opaque offers) to show rankings lower search costs, instead of affecting consumer expectations or utility. Finally, I show a utility-based ranking built on this model’s estimates benefits consumers and the search intermediary. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1072 .

In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated textual reviews into the user modeling and recommending process. Advanced text analysis and opinion mining techniques enable the extraction of various types of review elements, such as the discussed topics, the multi-faceted nature of opinions, contextual information, comparative opinions, and reviewers’ emotions. In this article, we provide a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques. The review-based recommender system’s ability to alleviate the well-known rating sparsity and cold-start problems is emphasized. This survey classifies state-of-the-art studies into two principal branches: review-based user profile building and review-based product profile building . In the user profile sub-branch, the reviews are not only used to create term-based profiles, but also to infer or enhance ratings. Multi-faceted opinions can further be exploited to derive the weight/value preferences that users place on particular features. In another sub-branch, the product profile can be enriched with feature opinions or comparative opinions to better reflect its assessment quality. The merit of each branch of work is discussed in terms of both algorithm development and the way in which the proposed algorithms are evaluated. In addition, we discuss several future trends based on the survey, which may inspire investigators to pursue additional studies in this area.

Online product reviews are significant in modern e-business because they can influence consumers’ purchase decisions. However, with the dramatic increase in the number of product categories and reviews, it is impossible for consumers to read all online reviews. In this paper, we design a novel method to help customers rank products using online reviews. Our method can be divided into three stages: generating a list of related alternative products based on specific filter conditions, collecting online reviews, and processing and measuring customer satisfaction. This study offers three significant improvements over previous approaches. First, we incorporate prospect theory that reflects the greater impact of negative reviews on customers’ purchase decisions to measure customer satisfaction concerning each attribute more accurately. Second, we combine the collective attribute weights calculated by entropy weight method (EWM) and the individual attribute weights given by a customer to improve VIKOR method, which can adjust the proportion of the two types of weights according to the customer’s knowledge of the product attributes. Third, for processing online reviews, we develop a new sentiment analysis algorithm that factors in the degree of consumer sentiment. This technique is different from the procedures used by existing studies for ranking products. To validate our method, we conduct a case study of automobile ranking and make some comparisons, which together demonstrate that the proposed method not only saves time and effort but also helps consumers select the products they really want.

Recently, sentiment analysis (SA) and multi-attribute decision making (MADA) have been extensively studied respectively, which aims to help decision makers make informed decisions. However, rather less attention has been paid to the field of combining SA and MADA. Therefore, in this paper, we propose a novel method to rank products through online reviews. To begin with, it is a novel idea to view different sentiment scores of one feature as the different membership degrees. Further, we propose the fuzzy sentiment word framework and corresponding computation rules to calculate the sentiment score of each feature in each review, which later can be used to obtain the overall performance of each feature concerning different products based on hesitant fuzzy set (HFS). Next, the attention degree of each feature is considered in the process of calculating weight of different features. In addition, based on 2-addiitive fuzzy measure and Choquet integral, we extend TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method, which concerns decision make's psychological behavior, to deal with criteria interactions (positive, mutual independent and negative) in the process of MADM. Furthermore, we use a case study to demonstrate the efficiency and applicability of the proposed method.

Online product ratings, as a type of electronic word-of-mouth, play an important role for helping consumers select desirable products, but it is difficult for consumers to read a large number of online ratings on e-commerce Web site. To support consumer’s purchase decision, how to rank the candidate products based on online product ratings and consumer’s preferences is a noteworthy research topic, while the existing studies concerning this issue are still relatively scarce. This paper proposes a method for ranking products based on online multi-attribute product ratings. In the method, a discrete percentage distribution of the evaluation of each candidate product with respect to each attribute based on online ratings is first constructed, and the 3 σ criterion is used to eliminate the anomalous ratings. Then, by defining of the stochastic dominance rules and the stochastic dominance degrees on comparing two discrete percentage distributions, the stochastic dominance relation between each pair of products is determined, and the corresponding stochastic dominance degree is calculated. Further, according to the obtained stochastic dominance degrees, the ranking of candidate products can be determined using the PROMETHEE-II method. A case study on selecting the automobile is given to illustrate the use of the proposed method.

Recently, in order to help consumers make decisions, ranking products with online reviews has become an interesting topic. However, literatures concerning this topic are really scare. Therefore, the paper proposes an extended TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method to rank products through online reviews. To begin with, the IF (intuitionistic fuzzy) based sentiment word framework and corresponding computation rules are constructed, where intuitionistic fuzzy set (IFS) is used to describe sentiment orientations and emotional intensity. Next, both frequency and attention degree of each feature are considered in calculating the feature weight. In addition, two-additive fuzzy measure, nonlinear programming, and Choquet integral are fully utilized to deal with positive, mutual independent, and negative criteria interactions. Finally, we use a case study to illustrate the proposed method and the results show that the proposed method can be effectively used to rank products through online reviews.

Businesses looking to engage and satisfy their online audience must prioritize the user experience in the quickly changing digital landscape. In order to address this challenge, this project proposes and implements a sophisticated algorithmic solution designed to produce extremely accurate and user-centric product rankings. This represents a groundbreaking approach. The system attempts to predict and present the most relevant product suggestions by meticulously considering a wide range of factors such as user preferences, historical interactions, product popularity trends, and user similarity metrics. Our methodology is distinguished by the use of a dynamic simulation environment in which user profiles, product categories, and interaction pat- terns are manipulated to replicate authentic real-world scenarios. The dynamic framework facilitates the comprehensive testing and optimization of customized ranking algorithms, guaranteeing their flexibility in response to changing user preferences and behaviors. The project’s effectiveness is evaluated using precise evaluation metrics that offer quantitative information about how well the system understands and responds to each unique user’s preference, ultimately resulting in a more fulfilling and rich online shopping or content consumption experience.

Massive online reviews provide consumers with the convenience of obtaining product information, but it is still worth exploring how to provide consumers with useful and reliable product rankings. The existing ranking methods do not fully mine user information, rating, and text comment information to obtain scientific and reasonable information aggregation methods. Therefore, this study constructs a user credibility model and proposes a large-scale user information aggregation method to obtain a new product ranking method. First, in order to obtain the aggregate weight of large-scale users, this paper proposes a consistency modeling method of text comments and star ratings by mining the associated information of user comments, including user interaction information and user personalized characteristics information, combined with sentiment analysis technology, and then constructs a user credibility model. Second, a double-layer group division mechanism considering user regions and comment time is designed to develop the large-scale group ratings aggregation approach. Third, based on the user credibility model and the large-scale ratings aggregation approach, a product ranking method is developed. Finally, the feasibility and effectiveness of the proposed method are verified through a case study for automobile ranking and a comparative analysis is furnished. The analysis results of the application case of automobile ranking show that there is a significant difference between the ranking results obtained by the ratings aggregation method based on the arithmetic mean and the ranking results obtained by this method. The method in this study comprehensively considers user credibility and group division, which can be reflected in user aggregation weights and the group aggregation process, and can also obtain more scientific and reasonable decision results.

The rapid growth of e-commerce has made it increasingly difficult for users to select appropriate products due to the overwhelming amount of available information. Although many platforms, such as Amazon and Rakuten, encourage users to leave reviews, effectively utilizing this information for personalized recommendations remains a challenge. To address this issue, we propose a multi-task product recommender system that supports both new users without purchase histories and existing users with interaction records. For new users without purchase histories, we introduce a ranking-based method that combines three market-oriented features: sales volume, sales period, and user satisfaction. User satisfaction is quantified using sentiment analysis of product reviews. These three factors are integrated into a composite score to identify products with a strong market presence and positive customer feedback. For existing users, we developed an enhanced neural collaborative filtering (NCF) method that incorporates a product bias factor. This model, named bias neural collaborative filtering (BNCF), utilizes multilayer perceptrons to learn latent user–product interactions while also capturing item popularity bias. We evaluated the proposed approaches using a real-world dataset from Rakuten. The results show that our multi-task system effectively improves recommendation quality for users in both cold-start and data-rich scenarios.