Personalized and Explainable Aspect-based Recommendation using Latent Opinion Groups

The problem of explainable recommendation—supporting the recommendation of a product or service with an explanation of why the item is a good choice for the user—is attracting substantial research attention recently. Recommendations associated with an explanation of how the aspects of the chosen item may meet the needs and preferences of the user can improve the transparency and trustworthiness of consumer-oriented applications, which is the motivation driving this research area. Current methods are far from ideal because they do not necessarily consider the following issues: users’ opinions are influenced not only by individual aspects but also by the dependency between sentiments towards aspect; not all users place the same value on all aspects; and, any explanation are not provided for how the item aspects have led to the recommendation. We introduce a personalized explainable aspect-based recommendation method that can address these challenges. To identify the aspects that a user cares about, our semantics-aware method learns the likelihood of an aspect being mentioned in a user’s review. To capture dependency between the users’ sentiments towards an aspect, reviews that express opinions with similar polarities towards sets of aspects are clustered together in latent opinion groups. To construct aspect-based explanations, item aspects are rated according to their importance based on these latent opinion groups and the preferences of the target user. Finally, to provide a user with a (set of) useful recommendation(s) of an item, our method selects and synthesizes the aspects important for the target user. We evaluate our method over two datasets from (a) Yelp and (b) Tripadvisor. Our results demonstrate that our method outperforms previous methods in both recommendation performance and explainability.