Personalised Opinion-based Recommendation

E-commerce recommender systems seek out matches betweencustomers and items in order to help customers discover more relevantand satisfying products and to increase the conversion rate of browsers tobuyers. To do this, a recommender system must learn about the likes anddislikes of customers/users as well as the advantages and disadvantages(pros and cons) of products. Recently, the explosion of user-generatedcontent, especially customer reviews, and other forms of opinionated expression,has provided a new source of user and product insights. Theinterests of a user can be mined from the reviews that they write andthe pros and cons of products can be mined from the reviews writtenabout them. In this paper, we build on recent work in this area to generateuser and product proles from user-generated reviews. We furtherdescribe how this information can be used in various recommendationtasks to suggest high-quality and relevant items to users based on eitheran explicit query or their prole. We evaluate these ideas using alarge dataset of TripAdvisor reviews. The results show the benets ofcombining sentiment and similarity in both query-based and user-basedrecommendation scenarios, and also disclose the eect of the number ofreviews written by a user on recommendation performance.