Ruihai Dong

Opinionated Product Recommendation

In this paper we describe a novel approach to case-based product recommendation. It is novel because it does not leverage the usual static, feature-based, purely similarity-driven approaches of traditional case-based recommenders. Instead we harness experiential cases, which are automatically mined from user generated reviews, and we use these as the basis for a form of recommendation that emphasises similarity and sentiment. We test our approach in a realistic product recommendation setting by using live-product data and user reviews.

Sentimental product recommendation

This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.

Combining similarity and sentiment in opinion mining for product recommendation

In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user has liked or purchased in the past. To do this, content-based methods must be able to compute the similarity between pairs of products (unseen products and liked products, for example) and typically this is achieved by comparing product features or other descriptive elements. The approach works well when product descriptions are readily available and when they are detailed enough to afford an effective similarity comparison. But this is not always the case. Detailed product descriptions may not be available since they can be expensive to create and maintain. In this article we consider another source of product descriptions in the form of the user-generated reviews that frequently accompany products on the web. We ask whether it is possible to mine these reviews, unstructured and noisy as they are, to produce useful product descriptions that can be used in a recommendation system. In particular we describe a novel approach to product recommendation that harnesses not only the features that can be mined from user-generated reviews but also the expressions of sentiment that are associated with these features. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers, and we demonstrate the practical benefits of this approach across a variety of Amazon product domains.

Further Experiments in Opinionated Product Recommendation

In this paper we build on recent work on case-based product recommendation focused on generating rich product descriptions for use in a recommendation context by mining user-generated reviews. This is in contrast to conventional case-based approaches which tend to rely on case descriptions that are based on available meta-data or catalog descriptions. By mining user-generated reviews we can produce product descriptions that reflect the opinions of real users and combine notions of similarity and opinion polarity (sentiment) during the recommendation process. In this paper we compare different variations on our review-mining approach, one based purely on features found in reviews, one seeded by features that are available from meta-data, and one hybrid approach that combines both approaches. We evaluate these approaches across a variety of datasets form the travel domain.

Towards an intelligent reviewer's assistant: recommending topics to help users to write better product reviews

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. Indeed this has become a vital part of the service provided by sites like Amazon and TripAdvisor, so much so that many of us will routinely check appropriate product reviews before making a purchase decision, regardless of whether we intend to purchase online or not. The importance of reviews has highlighted the need to help users to produce better reviews and in this paper we describe the development and evaluation of a Reviewers Assistant for this purpose. We describe a browser plugin that is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. These suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review. We describe and evaluate a number of different algorithms to identify useful topics to recommend to the user and go on to describe the results of a preliminary live-user trial.

Mining Features and Sentiment from Review Experiences

Supplementing product information with user-generated content such as ratings and reviews can help to convert browsers into buyers. As a result this type of content is now front and centre for many major e-commerce sites such as Amazon. We believe that this type of content can provide a rich source of valuable information that is useful for a variety of purposes. In this work we are interested in harnessing past reviews to support the writing of new useful reviews, especially for novice contributors. We describe how automatic topic extraction and sentiment analysis can be used to mine valuable information from user-generated reviews, to make useful suggestions to users at review writing time about features that they may wish to cover in their own reviews. We describe the results of a live-user trial to show how the resulting system is capable of delivering high quality reviews that are comparable to the best that sites like Amazon have to offer in terms of information content and helpfulness.

Harnessing the Experience Web to Support User-Generated Product Reviews

Today, online reviews for products and services have become an important class of user-generated content and they play a valuable role for countless online businesses by helping to convert casual browsers into informed and satisfied buyers. In many respects, the content of user reviews is every bit as important as the catalog content that describes a given product or service. As users gravitate towards sites that offer insightful and objective reviews, the ability to source helpful reviews from a community of users is increasingly important. In this work we describe the Reviewer’s Assistant, a case-based reasoning inspired recommender system designed to help people to write more helpful reviews on sites such as Amazon and TripAdvisor. In particular, we describe two approaches to helping users during the review writing process and evaluate each as part of a blind live-user study. Our results point to high levels of user satisfaction and improved review quality compared to a control-set of Amazon reviews.

From More-Like-This to Better-Than-This: Hotel Recommendations from User Generated Reviews

To help users discover relevant products and items recommender systems must learn about the likes and dislikes of users and the pros and cons of items. In this paper, we present a novel approach to building rich feature-based user profiles and item descriptions by mining user-generated reviews. We show how this information can be integrated into recommender systems to deliver better recommendations and an improved user experience.

Personalized Opinion-Based Recommendation

E-commerce recommender systems seek out matches between customers and items in order to help customers discover more relevant and satisfying products and to increase the conversion rate of browsers to buyers. To do this, a recommender system must learn about the likes and dislikes of customers/users as well as the advantages and disadvantages (pros and cons) of products. Recently, the explosion of user-generated content, especially customer reviews, and other forms of opinionated expression, has provided a new source of user and product insights. The interests of a user can be mined from the reviews that they write and the pros and cons of products can be mined from the reviews written about them. In this paper, we build on recent work in this area to generate user and product profiles from user-generated reviews. We further describe how this information can be used in various recommendation tasks to suggest high-quality and relevant items to users based on either an explicit query or their profile. We evaluate these ideas using a large dataset of TripAdvisor reviews. The results show the benefits of combining sentiment and similarity in both query-based and user-based recommendation scenarios, and also disclose the effect of the number of reviews written by a user on recommendation performance.

User-based Opinion-based Recommendation

Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Australia, August 19-25 2017