Enhancing the Sustainability of a Location-Aware Service through Optimization

Abstract

A location-aware service (LAS) is an imperative topic in ambient intelligence; an LAS recommends suitable utilities to a user based on the user's location and context. However, current LASs have several problems, and most of these services do not last. This study proposes an optimization-based approach for enhancing the sustainability of an LAS. In this paper, problems related to optimizing an LAS system are presented. The distinct nature of an LAS optimization problem in comparison with traditional optimization problems is subsequently described. Existing methods applicable to solving an LAS optimization problem are also reviewed. The advantages and disadvantages of each method are then discussed as a motive for combining multiple optimization methods in this study, as illustrated by an example. Finally, opportunities and challenges faced by researchers in this field are presented.

Ubiquitous Hotel Recommendation Using a Fuzzy-Weighted-Average and Backpropagation-Network Approach

“…Ubiquitous hotel recommendation is one of the most widely applied location‐aware services (LASs) used to recommend suitable hotels to travelers based primarily on their location, among other variables . The location of a traveler is typically detected using the global positioning system (GPS) module on his/her cell phone, which is subject to imprecision . In addition, travelers’ requirements for a suitable hotel vary considerably, and this is often addressed by requesting travelers to input their requirements through a client‐side application, also known as an app.…”

“…However, if the questions are too few, they may not completely address each traveler's concerns. Concerns typically vary among travelers. A traveler may not answer each question. Data incompleteness is one of the major hindrances to the smooth operation of an LAS system and can be approached in two ways . One is to consider attributes with complete data before considering attributes without complete data.…”

“…A higher weight was assigned to a hotel attribute that had been mentioned more frequently. Tsai and Chen determined two modes for incorporating traveler location into the reasoning process: the “transportation planning in advance” mode that considers distance as an additional hotel attribute, and the “transportation planning afterwards” that does not.…”

“…1 The location of a traveler is typically detected using the global positioning system (GPS) module on his/her cell phone, which is subject to imprecision. 2 In addition, travelers' requirements for a suitable hotel vary considerably, and this is often addressed by requesting travelers to input their requirements through a client-side application, also known as an app. However, designing such an interface is challenging for the following reasons: (1) A traveler has no time or is unwilling to answer many questions.…”

“…Data incompleteness is one of the major hindrances to the smooth operation of an LAS system and can be approached in two ways. 2 One is to consider attributes with complete data before considering attributes without complete data. The other is to guess the missing data by using historical records.…”