Abstract
In the Web 2.0 era, the problem of uneven quality and overload of online reviews is very serious, and the cognitive cost of obtaining valuable content from them is getting higher and higher. This paper explores an effective solution to address comment overload by means of information recommendation in order to improve the utilization of online information and information service quality. This paper proposes a review ranking recommendation scheme that focuses on the information quality of reviews and places more emphasis on satisfying users’ personal information need. The paper’s approach is used to extract and rank low-frequency keywords that appear only once in the comment set. The more useful the extracted phrases are, the more useful this review will be and the higher the usefulness votes will be, which can reflect the actual situation of this product more objectively and accurately and facilitate better consumption decisions for consumers. The experimental results show that users’ satisfaction with the perceived usefulness of the reviews is jointly influenced by the information quality of Meituan’s reviews and users’ individual information needs; the recommendation strategy achieves the organic integration of the two, and the evaluation results under three different recommendation modes show that compared with “interest recommendation” and “utility recommendation,” the satisfaction score of “fusion recommendation” is the highest
Highlights
With the rapid growth of the Internet and e-commerce platforms in recent years, the usefulness of online reviews has become an important influencing factor in consumer decision making [1]
Users can learn about merchants’ products and services through online reviews, which help them make better consumer decisions and reduce the reference cost of products and services. e famous Jupiter Research company, through years of research and analysis, found that 75% of consumers refer to reviews on the Internet before spending money on dining, travel, and accommodation, purchasing goods, parent-child playgrounds, and many other things. e same is true in China, with platforms such as Taobao, Jingdong, Meituan, and Where to Go [2]
Due to the openness of the Internet, the cost of posting online reviews is very low, and a lot of spam and false information make the quality of information in reviews vary, resulting in a large number of reviews, which is noisy and difficult to distinguish, and there are many ways of reviews and different language expressions, and some reviews do not bring us useful reference value [3]
Summary
With the rapid growth of the Internet and e-commerce platforms in recent years, the usefulness of online reviews has become an important influencing factor in consumer decision making [1]. Users can learn about merchants’ products and services through online reviews, which help them make better consumer decisions and reduce the reference cost of products and services. Ese filtering strategies focus on information quality and help users quickly access useful information by placing high-quality reviews at the top. These filtering strategies do not focus on satisfying individual users’ needs [5]. We propose a low-frequency keyword extraction method for review usefulness voting. E main purpose is to identify low-frequency keywords from the reviews of Meituan and to provide consumers with more choices and decisions through the study of usefulness voting, instead of just looking at the star rating given by users as the judgment index (usually five stars). Based on the above difficulties, there are still no more studies on the effectiveness of comment voting, which will become a key topic for our research
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