Comparison of Sentiment Analysis and User Ratings in Venue Recommendation

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

Enhancing itinerary recommendation with linked open data

This paper proposes a recommender system that exploits linked open data (LOD) to perform a social context-aware cross-domain recommendation of personalized itineraries integrated with multimedia and textual content. To this aim, the recommendation engine considers the user profile, the context of use, and the features of the points of interest (POIs) extracted from LOD sources. We describe how to extract data and process it to perform hybrid filtering. All recommendations are based on the user’s features extracted implicitly and explicitly. These features are used to apply content-based filtering and collaborative filtering, weighing results based on similar users’ experience. The preliminary results of an experimental evaluation on a sample of 20 real users show the effectiveness of the proposed system not only in terms of perceived accuracy, but also in terms of novelty, non-obviousness, and serendipity

An Approach to Conversational Recommendation of Restaurants

In this paper, we propose an approach based on the integration of a chatbot module, a location-based service, and a recommendation algorithm. This approach has been deployed for restaurant recommendation, tested on a sample of 50 real users, and compared with some state-of-the-art algorithms. The preliminary experimental results showed the benefits of the proposed approach in terms of performance. An ANOVA test enabled us to verify the statistical significance of the obtained findings