Comparison of Sentiment Analysis and User Ratings in Venue Recommendation

Abstract

Venue recommendation aims to provide users with venues to visit, taking into account historical visits to venues. Many venue recommendation approaches make use of the provided users' ratings to elicit the users' preferences on the venues when making recommendations. In fact, many also consider the users' ratings as the ground truth for assessing their recommendation performance. However, users are often reported to exhibit inconsistent rating behaviour, leading to less accurate preferences information being collected for the recommendation task. To alleviate this problem, we consider instead the use of the sentiment information collected from comments posted by the users on the venues as a surrogate to the users' ratings. We experiment with various sentiment analysis classifiers, including the recent neural networks-based sentiment analysers, to examine the effectiveness of replacing users' ratings with sentiment information. We integrate the sentiment information into the widely used matrix factorization and GeoSoCa multi feature-based venue recommendation models, thereby replacing the users' ratings with the obtained sentiment scores. Our results, using three Yelp Challenge-based datasets, show that it is indeed possible to effectively replace users' ratings with sentiment scores when state-of-the-art sentiment classifiers are used. Our findings show that the sentiment scores can provide accurate user preferences information, thereby increasing the prediction accuracy. In addition, our results suggest that a simple binary rating with 'like' and 'dislike' is a sufficient substitute of the current used multi-rating scales for venue recommendation in location-based social networks.

Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classification

“…In doing so, we postulate that removing noisy or unlabelled reviews and replacing them with further positive reviews as identified by NCWS results into a more effective learned Deep-CoNN model. We validate this through experiments on the Yelp dataset, which is a widely used venue recommendation dataset [41].…”

Leveraging Review Properties for Effective Recommendation

“…Unlike previous work [2,4], which used an attention mechanism to learn the usefulness of reviews, we argue that the review properties can be directly leveraged within an attention mechanism to effectively capture the usefulness of reviews. Moreover, there are many existing approaches [24,32,44] that extract the review properties and integrate them as side or contextual information to enhance the recommendation performance. However, unlike our work in this paper, such approaches do not make use of the reviews themselves.…”

“…Another property is whether a review expresses a sentiment. For instance, Wang et al [44] replaced the explicit ratings provided by users with the review sentiment scores to enhance the recommendation performance. The temporal and age properties of reviews have been integrated into various recommendation models, especially into the sequential recommendation models [24,47,52].…”

“…These review properties are associated with the historical reviews as well as the unseen reviews by the trained model. A number of studies [32,44] have previously attempted to leverage the review properties when making recommendations. The underlying premise of such studies is that the review properties encapsulate rich information about both the users' preferences and the items' attributes.…”

A Hybrid Approach for the Recommendation of Scholarly Journals

“…Journals are the venues where authors can publish their manuscripts for the use of community. Choosing the right scholarly journal to submit a paper is a primary concern for researchers and they need to know about several characteristics of scholarly Journals that are difficult to obtain [11] including aim and scope, acceptance rate, publication matrices, impact, aim and scope and article processing time [16]. On the basis of aim and scope journals differ from each other as all journals have their own aim and scope that defines their areas of publications.…”