ecommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Our process builds a collection of relevant consumer product reviews. Technically, the procedure for collecting reviews follows the algorithms for automated news extraction from news sites. 1 Once the product opinions mining base is populated, we employ text-mining techniques to extract useful information from review comments. Here, we discuss the overall framework for automating the use of consumer reviews and the framework's individual components. Where possible, we've used existing algorithms (for example, in the text-mining process) because our goal is to demonstrate our approach's strengths. Our recommender process involves several steps. For review information to be useful for the recommendation process, we must translate it into a structured form and communicate it to the process in a form suitable for generating recommendations. We've developed and employed an ontology to translate opinions' quality and content into a form the recommender process can use. The text-mining process automatically maps the review comments into the ontology's information structure. A ranking mechanism computes a product's rating using the information from the consumer reviews stored in the ontology. It prioritizes that information with respect to the consumer's level of expertise in using the product under consideration. The recommender system makes a recommendation based on the ontology data. Therefore, the recommendation quality depends on accurately mapping the proper knowledge from the semantic features in the review comments into the ontology structure. Figure 1 shows our proposed system's overall process structure, and the following sections outline the steps involved. Our first step was to find a suitable tool for extracting the information in the text and converting it into structured data. Identifying an appropriate representation of consumer opinions that the system can use is a key problem. One way to convert these opinions to a structured form is to use a translation ontology, which is typically used as a form of knowledge representation and sharing. In this application, the  Content-based filtering methods use information about the item itself to make suggestions, rather using than information about other consumers' preferences. Such systems emulate the behavior of a consumer recommending a product to a friend because he or she has used the product and knows the friend's preferences in terms of product features. Content-based recommender systems uniquely characterize each consumer without having to match his or her interests to other consumers. They can provide a list of content features that explain why an item has been recommended. Such a list can strengthen consumer confidence in the recommendation and reflect the consumer's own preferences. In the contentbased approach, consumers can provide some initial information about the product to assist the system. Collaborative filtering makes recommendations about the preferences of a user (filtering) on the basis of the other users' collective taste information (collaborative). The underlying assumption of such methods is that those who agreed in the past also tend to agree again in the future. 1 In other words, these systems emulate the behavior of a consumer recommending a product to a friend because other consumers that she knows, and believes have tastes similar to her friend's, like the product. Technically, such a system operates similarly to a case-basedreasoning system, without the adaptation step. It maintains a case base of individual consumers' preferences, finds other consumers whose known preferences correlate significantly with the user's, and recommends to the user other items that matched patrons enjoyed. The system can give the user a list of some of these patrons and their previous purchases to provide an explanation and make the user more confident about the recommendation. This approach requires a sufficient number of consumer ratings. Collaborative-filtering systems use a collection of historical rating data of m users on n products as input, which they collect by asking users to rate products on a scale. 2 Collecting such ratings requires the consumer to spend time responding, and the actual values might not necessarily provide reliable estimates of consumer preferences. Another issue such systems face is how to recommend products that haven't been rated by enough consumers. The traditional collaborative-filtering approach doesn't provide effective recommendation strategies. Collaborative filtering and content-based filtering perform unsatisfactorily without large amounts of usage data, which discourages users from using the system, and the system's performance can't be improved without sufficient user participation. 3 To solve this problem, we use reviews from experienced consumers that are already available on the Internet to determine the most popular product according to the given criteria. Rather than completing forms with rating values, many consumers prefer to use natural language and express their opinions about the product in a free-text form, similar to a conversation with a friend. In the online world, consumers can exchange their experiences with a product in several ways: 4,5 product review forums, virtual-community logs, product discussion boards, and e-commerce Web sites. There's growing evidence that such forums inform and influence consumers' purchase decisions. 6,7 Decision makers use expert advice either to make their decisions more accurate or to reduce their effort expenditure. 8 Despite the importance and value of such information, no comprehensive mechanism exists that formalizes selecting, retrieving, and using opinions. Part of the problem resides in the complexity of extracting information from text data and converting it into product recommendation. Gediminas Adomavicius provided an overview of recent developments in recommender systems. 3 According to this review, recommender systems that use review comments using text mining techniques are yet to be developed. Francesco Ricci and René Wietsma proposed using review comments to give some explanation about recommendations made. 9,10 They believed the review comments could be widely used in recommender systems and result in better recommendations. So far, Ricci and Wietsma seem to have created the only recommender system that integrates reviews in the recommendation process. 9,10 They use product reviews in the product selection decision process for a mobile recommender system, employing social-filtering algorithms to extract knowledge from the reviews. Their system aims to improve the explanation of the recommendation by providing relevant reviews from users with similar tastes. The reviews are used to explain the recommendations but not to actually make recommendations.
Aciar et al. REF implemented a system to make recommendations based on the ontological structure of freetext reviews.
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
IEEE Intelligent Systems
Computer Science