Conference Paper

CompRec-Trip: A composite recommendation system for travel planning.

DOI: 10.1109/ICDE.2011.5767954 Conference: Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, April 11-16, 2011, Hannover, Germany
Source: DBLP


Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or a DVD. However, applications such as travel planning can benefit from a system capable of recommending packages of items, under a user-specified budget and in the form of sets or sequences. In this context, there is a need for a system that can recommend top-k packages for the user to choose from. In this paper, we propose a novel system, CompRec-Trip, which can automatically generate composite rec- ommendations for travel planning. The system leverages rating information from underlying recommender systems, allows flex- ible package configuration and incorporates users' cost budgets on both time and money. Furthermore, the proposed CompRec- Trip system has a rich graphical user interface which allows users to customize the returned composite recommendations and take into account external local information. I. INTRODUCTION


Available from: Laks Lakshmanan
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    • "The system will select a series of tourist spots according to the information given during user registration, the information input for the current trip, and the preferences of other users. Xie et al. [10] proposed a planning method for travel to tourist spots that also helps users to quickly identify destinations using the RS. Unlike the method proposed by Garica et al., Min's system selects the top í µí±˜ tourist spots with the highest scores among the assessed spots in the manycomponent RS and external database to form a package, which is the travel itinerary. "
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    • "Smart transportation is expected to play an important role to meet the growing demand of various transportationrelated services from citizens [16] and government officers [2], especially in modern cities. A fundamental requirement to smart transportation is to collect the dynamic vehicular location data to form the basis to build an effective traffic information system [4]. "
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    ABSTRACT: Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow, given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend the top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender systems focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of decidingwhether there is a recommendation (package)whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommendations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.
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