Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

Abstract

Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.

Evaluating Vector Representations from User's Reviews in a Recommendation Task

“…User reviews are natural language texts, in which users write about their opinions regarding different items' features. Several research [Almahairi et al 2015, Zheng et al 2017, Baral et al 2018, Sundermann et al 2019 aggregates this information into the recommendation process, using different strategies to represent them in this process. The most common representation model is the bag-of-words model, but it creates high dimensionality representations, which may impair the performance of the recommendations.…”

“…For the last few years, considering user reviews in recommendation systems has been a hot research topic, since several studies [Almahairi et al 2015, Zheng et al 2017, Baral et al 2018, Sundermann et al 2019 have been reporting an increase in the quality of recommendations when using such information. In order to aggregate user's reviews in the recommendation process, it's necessary to extract relevant information from these texts and several methods have been used for this purpose, such as the traditional bag-of-words method [Almahairi et al 2015], aspects extraction [Baral et al 2018], topics modelling [McAuley and Leskovec 2013] and word embeddings [Zheng et al 2017].…”

“…if the items are movies, these features could be the actors, director, the total duration of the movie, and several other relevant entities that represent this movie). Since the ultimate goal of recommendation systems is to predict user preference and these texts contain such information, user's reviews are extremely valuable in these systems [McAuley and Leskovec 2013, Zheng et al 2017, Guo et al 2017, Baral et al 2018, Sundermann et al 2019. Thus, in this work, our goal is to extract these relevant features of user reviews, creating items representations from these texts, which will then be aggregated in the recommendation process, alongside the rating data.…”

Text-based Emotion Aware Recommender

“…Illustrated below are samples of a few recent works. Orellana-Rodriguez [10] [11] advocated that instead of detecting the affective polarity features (i.e., positive/negative) of a given short video in YouTube, they detect the paired eight basic human emotions advocated by Plutchik [12] [13] into four opposing pairs of basic moods: joy-sadness, anger-fear, trust-disgust, and anticipation-surprise. Orellana-Rodriguez [10] also leveraged the auto extraction of film metadata's moods context for making emotion-aware movie recommendations.…”

Editorial for the Special Issue on “Modern Recommender Systems: Approaches, Challenges and Applications”

“…Finally, in the paper "Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review" [7], the authors recognize the importance of the rich information embedded in the reviews/texts of users in the context of opinion mining and contextual information extraction techniques for recommender systems. To this end, they present a systematic and comprehensive review on recommender systems that explores both contextual information and opinion mining.…”