Cocktail Approach For Travel Package - Java

Abstract :- A Cocktail Approach for Travel Package Recommendation :- :- Java Project :- :- 

Recent years have witnessed an increased interest in recommended systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation.

Existing System

There are many technical and domain challenges inherent in designing and implementing an effective recommender system for personalized travel package recommendation.

1. Travel data are much fewer and sparser than traditional items, such as movies for recommendation, because the costs for a travel are much more expensive than for watching a movie.

2. Every travel package consists of many landscapes (places of interest and attractions), and, thus, has intrinsic complex spatio-temporal relationships. For example, a travel package only includes the landscapes which are geographically colocated together. Also, different travel packages are usually developed for different travel seasons. Therefore, the landscapes in a travel package usually have spatial temporal autocorrelations. 

3. Traditional recommender systems usually rely on user explicit ratings. However, for travel data, the user ratings are usually not conveniently available.

System Architecture

Disadvantages Of Existing System

·        Recommendation has a long period of stable value.

·        To replace the old ones based on the interests of the tourists.

·        A values of travel packages can easily depreciate over time and a package usually only lasts for a certain period of time

Proposed System

In this paper, we aim to make personalized travel package recommendations for the tourists. Thus, the users are the tourists and the items are the existing packages, and we exploit a real-world travel data set provided by a travels for building recommender systems. we develop a tourist-area-season topic (TAST) model, which can represent travel packages and tourists by different topic distributions. In the TAST model, the extraction of topics is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes.  Based on this TAST model, a cocktail approach is developed for personalized travel package recommendation by considering some additional factors including the seasonal behaviors of tourists, the prices of travel packages, and the cold start problem of new packages.

Advantages Of Proposed System

·        Represent the content of the travel packages and the interests of the tourists.

·        TAST model can effectively capture the unique characteristics of travel data.

·        The cocktail recommendation approach performs much better than traditional techniques.

For More Details About this Project Please Contact

Logic Systems

Hyderabad - 500038

Email id: logicsystems24x7@gmail.com

Website :- www.logicsystems.org.in

System Requirements

Hardware Requirements

·        System                    :       Pentium IV 2.4 GHz.

·        Hard Disk                :       40 GB.

·        Floppy Drive            :       1.44 Mb.

·        Monitor                    :       15 VGA Colour.

·        Mouse                      :       Logitech.

·        Ram                         :       512 Mb.

Software Requirements

·        Operating system     :       Windows XP/7.

·        Coding Language     :       JAVA/J2EE

·        IDE                          :       Netbeans 7.4

·        Database                  :       MYSQL