2 research outputs found
A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
In popular applications such as e-commerce sites and social media, users
provide online reviews giving personal opinions about a wide array of items, such
as products, services and people. These reviews are usually in the form of free text,
and represent a rich source of information about the users’ preferences. Among the
information elements that can be extracted from reviews, opinions about particular
item aspects (i.e., characteristics, attributes or components) have been shown to be
effective for user modeling and personalized recommendation. In this paper, we investigate
the aspect-based recommendation problem by separately addressing three
tasks, namely identifying references to item aspects in user reviews, classifying the
sentiment orientation of the opinions about such aspects in the reviews, and exploiting
the extracted aspect opinion information to provide enhanced recommendations. Differently
to previous work, we integrate and empirically evaluate several state-of-the-art
and novel methods for each of the above tasks. We conduct extensive experiments
on standard datasets and several domains, analyzing distinct recommendation quality
metrics and characteristics of the datasets, domains and extracted aspects. As a result
of our investigation, we not only derive conclusions about which combination of methods
is most appropriate according to the above issues, but also provide a number of
valuable resources for opinion mining and recommendation purposes, such as domain
aspect vocabularies and domain-dependent, aspect-level lexiconsThis work was supported by the Spanish Ministry of Economy, Industry and Competitiveness
(TIN2016-80630-P)
Recommendation using textual opinions
Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize the ranking she is shown. We propose a new technique, topic profile collaborative filtering, where we build user profiles from users' review texts and use these profiles to filter other review texts with the eyes of this user. We verify on data from an actual review site that review texts and topic profiles indeed correlate with ratings, and show that topic profile collaborative filtering provides both a better mean average error when predicting ratings and a better approximation of user preference orders