THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.Comment: Accepted by KDD 201

Hybrid recommendation system using product reviews

Abstract. Several businesses/smart applications rely on personalizing their services to adapt to the user’s preferences. Personalized services are developed using recommendation systems based on user’s feedback on products/services, needs, habits and social or demographic characteristics. Several businesses from e-commerce (suggesting users what to buy) to hospitality services (suggesting which hotel to book) focus on using recommendation systems to achieve a personalized experience for their users. Majority of recommendation systems make use of only product ratings shared by the users, this may pose challenges like sparsity of ratings. The wide availability of other attributes of products or users like textual product reviews provided by users or product descriptions in e-commerce and hospitality domains present a gold mine of additional personalising information with which to supplement their ratings based recommendation system. Recommendation systems majorly involves two tasks: rating (predict ratings that user might assign to a product) and ranking (recommend products based on predicted rank scores) prediction tasks. In this thesis, we propose a novel hybrid recommendation system using the state-of-the-art DeepFM model which makes use of multiple textual features derived from product reviews particularly contextual sentence embedding vectors, average sentiment scores and linguistic cues such as presence/absence of negation in the product reviews in combination with ratings shared by users to enhance the prediction of the desired ratings or rank scores. We evaluated our system with commercial datasets from Amazon and Datafiniti for both tasks: predicting rating and recommendations based on predicted rank scores. We utilised different metrics for both types of tasks. From our evaluation we infer that using contextual sentence embedding vectors extracted using BERT, average sentiment scores and presence/absence of negation in the product reviews obtained from VADER, does impact the prediction of ratings and recommendations based on predicted scores of the recommendation system which only utilises product ratings as user preferences. Furthermore, we can conclude from our evaluation that (A) contextual embedding vectors and average sentiment scores together along with ratings in the proposed hybrid system improves prediction of desired ratings, (B) contextual embedding vectors, average sentiment scores and presence/absence of negation in the product reviews together along with ratings in the proposed hybrid system improves prediction of desired ratings as well, (C) contextual embedding vectors and average sentiment scores together along with ratings in the proposed hybrid system improves recommendations based on rank scores