A New Effective Subject Extraction for Travel Package Suggestions

The new idea suggestion framework is applying in numerous applications.in this task investigate the online travel data of visitors to give customized travel bundle. Be that as it may, conventional suggestion framework cannot giving better travel bundle to sightseers from different geo-realistic areas. Numerous specialized difficulties are accessible for planning and execution of proficient travel bundle suggestion framework. Proposing another model named as traveller region season point model alongside Latent Dirichlet Allocation algorithm which extricates the elements like areas, travel seasons of different scenes. Presenting cocktail approach for better customized travel bundle proposal. Further Extending TAST model with the vacationer connection territory season subject model incorporates relationship among the visitors. In the long run our proposed methodology is effective to give better bundle suggestion for travellers

Comparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity: Which Contextual Modeling, Pre-filtering and Post-Filtering Methods Perform the Best

Although the area of Context-Aware Recommender Systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.Politecnico di Bari, Italy; NYU Stern School of Busines

Profit Maximization with Sufficient Customer Satisfactions

In many commercial campaigns, we observe that there exists a tradeoff between the number of customers satisfied by the company and the profit gained. Merely satisfying as many customers as possible or maximizing the profit is not desirable. To this end, in this article, we propose a new problem called k - &lt;underline&gt;S&lt;/underline&gt;atisfiability &lt;underline&gt;A&lt;/underline&gt;ssignment for &lt;underline&gt;M&lt;/underline&gt;aximizing the &lt;underline&gt;P&lt;/underline&gt;rofit ( k -SAMP), where k is a user parameter and a non-negative integer. Given a set P of products and a set O of customers, k -SAMP is to find an assignment between P and O such that at least k customers are satisfied in the assignment and the profit incurred by this assignment is maximized. Although we find that this problem is closely related to two classic computer science problems, namely maximum weight matching and maximum matching, the techniques developed for these classic problems cannot be adapted to our k -SAMP problem. In this work, we design a novel algorithm called Adjust for the k -SAMP problem. Given an assignment A , Adjust iteratively increases the profit of A by adjusting some appropriate matches in A while keeping at least k customers satisfied in A . We prove that Adjust returns a global optimum. Extensive experiments were conducted that verified the efficiency of Adjust . </jats:p

An intelligent hybrid multi-criteria hotel recommender system using explicit and implicit feedbacks

Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This dissertation aims to design a highly accurate hotel recommender system, implemented in various layers and tailored for the subject problem. Using multi-rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system

An intelligent destination recommendation system for tourists.

Choosing a tourist destination from the information available is one of the most complex tasks for tourists when making travel plans, both before and during their travel. With the development of a recommendation system, tourists can select, compare and make decisions almost instantly. This involves the construction of decision models, the ability to predict user preferences, and interpretation of the results. This research aims to develop a Destination Recommendation System (DRS) focusing on the study of machine-learning techniques to improve both technical and practical aspects in DRS. First, to design an effective DRS, an intensive literature review was carried out on published studies of recommendation systems in the tourism domain. Second, the thesis proposes a model-based DRS, involving a two-step filtering feature selection method to remove irrelevant and redundant features and a Decision Tree (DT) classifier to offer interpretability, transparency and efficiency to tourists when they make decisions. To support high scalability, the system is evaluated with a huge body of real-world data collected from a case-study city. Destination choice models were developed and evaluated. Experimental results show that our proposed model-based DRS achieves good performance and can provide personalised recommendations with regard to tourist destinations that are satisfactory to intended users of the system. Third, the thesis proposes an ensemble-based DRS using weight hybrid and cascade hybrid. Three classification algorithms, DT, Support Vector Machines (SVMs) and Multi- Layer Perceptrons (MLPs), were investigated. Experimental results show that the bagging ensemble of MLP classifiers achieved promising results, outperforming baseline learners and other combiners. Lastly, the thesis also proposes an Adaptive, Responsive, Interactive Model-based User Interface (ARIM-UI) for DRS that allows tourists to interact with the recommended results easily. The proposed interface provides adaptive, informative and responsive information to tourists and improves the level of the user experience of the proposed system