You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper, we present a real-world conversational AI system to search for and book hotels through text messaging. Our architecture consists of a frame-based dialogue management system, which calls machine learning models for intent classification, named entity recognition, and information retrieval subtasks. Our chatbot has been deployed on a commercial scale, handling tens of thousands of hotel searches every day. We describe the various opportunities and challenges of developing a chatbot in the travel industry.

Cited Abstract(s):
[1]: 

[2]: User-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers' multidimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique data set of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. By doing so, we can provide customers with the “best-value” hotels early on. Our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. On a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. We thus highlight the tight linkages between user behavior on social media and search engines. Our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research.

[3]: As the widespread use of computers and the high-speed development of the Internet, E-Commerce has already penetrated as a part of our daily life. For a popular product, there are a large number of reviews. This makes it difficult for a potential customer to make an informed decision on purchasing the product, as well as for the manufacturer of the product to keep track and to manage customer opinions. In this paper, we pay attention to online hotel reviews, and propose a supervised machine learning approach using unigram feature with two types of information (frequency and TF-IDF) to realize polarity classification of documents. As shown in our experimental results, the information of TF-IDF is more effective than frequency.

[4]: Recommender system offers personalized suggestions by analyzing user preference. However, the performance falls sharply when it encounters sparse data, especially meets a cold start user. Hotel is such kind of goods that suffers a lot from sparsity issue due to extremely low rating frequency. In order to handle these issues, this paper proposes a novel hotel recommendation framework. The main contribution includes: 1) We combine collaboration filtering (CF) with content-based (CBF) method to overcome sparsity issue, while ensuring high accuracy. 2) Travel intents are introduced to provide additional information for user preference analysis. 3) To provide as broad as possible recommendations, diversity techniques are employed. 4) Several experiments are conducted on the real Ctrip1 dataset, the results show that the proposed hybrid framework is competitive against classical approaches.
Natural language processing has been applied to other problems in the travel industry, for example, text mining hotel information from user reviews for a recommendation system [4] , or determining the economic importance of various hotel characteristics [2] . Sentiment analysis techniques have been applied to hotel reviews for classifying polarity [3] and identifying common complaints to report to hotel management [1] .