Personalized Travel Package Recommendation Using Cocktail Algorithm
نویسندگان
چکیده
79 Abstract— As the worlds of business, entertainment, travel and Internet technology become more linked, new types of business data become available for creative use and formal analysis. This project provides a study of online travel information for personalized travel package suggestion to the best course of travel. A target along this line is to address the unique characteristics of travel data, which differentiates travel packages from traditional items for recommendation. The characteristics of the travel packages, tourist feedback, season are analyzed and used for proposing on personalized travel package recommendation. A tourist-area-season topic (TAST) model is developed to 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. This also provides the tourist information and tourist feedbacks to evaluate a package for recommendation. The experimental results show that the approach is thus much more effective than traditional recommendation methods for travel package recommendation.
منابع مشابه
Recommendation and Ranking of Travel Package to Tourist
In a recent years Tourism and Travel stores offering a huge quantity of services and traveling information by online. Additionally, this huge volume of information smoothly accessed by electronic devices, like phone, computer with the availability of internet connection. When tourists are visiting any cities, most of them aimed to explore the interesting fact or things about the places and even...
متن کاملIndividualized Travel Recommendation by Mining People Ascribes and Travel Logs Types from Community Imparted Pictures
Leveraging community imparted data for personalized recommendation is one of the active research problems since there are rich contexts and human activities in such explosively growing data. We focus on personalized travel recommendation and show promising applications. We conduct personalized travel recommendation by considering specific user profiles or attributes. We propose a personalized t...
متن کاملPrediction of Sequencial Travel Route Recommendation for Journey Planning
Large amount of data can be collected from the Internet and travel guides, but these resources normally recommend personalized Point of Interest (POI) that is considered to be familiar, but they do not provide sufficient information to the interest preference of the users or hold to their trip constraints. The resources collected from the Internet and travel guides, normally recommend familiari...
متن کاملPersonalized POI Travel Recommendation from Social Media
Travel based recommendation and journey planning are challenging tasks because of various interest preferences and trip restrictions such as limitation of time, source and destination points for each tourist. Large amount of data can be collected from the Internet and travel guides, but these resources normally recommend individual Point of Interest (POI) that is considered to be familiar, but ...
متن کاملPersonalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations
Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PERSTOUR for recommending personalized tours using POI popularity and user interest preferences, which are automaticall...
متن کامل