Exploring IoT Location Information to Perform Point of Interest Recommendation Engine: Traveling to a New Geographical Region

Yang, Xu; Zimba, Billy; Qiao, Tingting; Gao, Keyan; Chen, Xiaoya

doi:10.3390/s19050992

Cited by 8 publications

(10 citation statements)

References 40 publications

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“…They performed their experiment on public TripAdvisor hotel-review datasets and the results provided valuable insights into the viewpoints of hotel guests and suggested further investigation in this direction. Yang et al [15] presented their effort at constructing a location-aware recommendation system that can model user preferences mainly based on the reviews of the users. They used datasets provided by Yelp.…”

Section: Related Workmentioning

confidence: 99%

An Effective Hotel Recommendation System through Processing Heterogeneous Data

et al. 2021

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Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework.

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Section: Related Workmentioning

confidence: 99%

An Effective Hotel Recommendation System through Processing Heterogeneous Data

et al. 2021

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“…Furthermore, another category of existing studies proposes route recommendation methods that include multiple tourism spots [24][25][26] (category 7). These methods have the advantage of being less troublesome for users, but have the disadvantage of providing little diversity of tourism patterns.…”

Section: Existing Work On Recommendation Of Tourism Spotsmentioning

confidence: 99%

On-site Trip Planning Support System Based on Dynamic Information on Tourism Spots

et al. 2020

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Recently, due to the drastic increase in foreign tourists coming to Japan, there has been a demand to provide smart tourism services that enable inbound tourists to comfortably enjoy sightseeing. To provide satisfactory experiences for tourists, it is desirable to provide tourist information in a timely manner by considering dynamic information, which is information that changes over time, such as current congestion information in destination spots and travel route information, in addition to static information, such as the preferences and profiles of tourists. However, in many existing systems, serious problems occur, such as (1) a lack of support for on-site use, (2) a lack of consideration of dynamic information, and (3) heavy burden on tourists. In this paper, we propose a novel system that can provide tourism plans for tourism spots in a timely manner. The proposed system consists of the following two key mechanisms: (A) A mechanism for acquiring preference information from tourists (including preference on dynamic information); (B) a curation mechanism for realizing on-site tourism. To demonstrate the effectiveness of the proposed system, we carried out evaluation experiments utilizing real tourism spots and simulations. As a result, we obtained the following primary findings: (1) On-site tourism spot recommendation is effective for tourists who do not make detailed tourism plans before sightseeing; (2) preference information for participants can be reflected in the tourism spot recommendation while massively reducing the burden on participants; (3) it is possible to obtain a higher satisfaction level than is achieved with model courses, which are often used for sightseeing.

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“…When this scenario happens, traditional recommendation methods might fall short, thus as per requirement of enhance people's living experience in smart city construction, new kinds of recommendation engine is needed. In 2019, Yang et al presented their effort at constructing a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting [19]. However, they have only included the review text for comprehending of people's preference, which might prove to be not enough.…”

Section: Related Workmentioning

confidence: 99%

“…where i and j have a non-zero R i,j rating in Rating matrix R, and λ is a weight that controls the capability of U , P, b in order to avoid over-fitting at which we use U i , P j , b i and b j as smoothness regularization terms. More details could be found in [19], we only include the necessary information here.…”

Section: Framework Of the Context-based Poi Recommendation Enginmentioning

confidence: 99%

“…Item similarity is computed by cosine similarity of ratings and we set the neighborhood size k = 150; 3) User Cluster(UC) [21]: A method clusters users into several communities(groups) for recommendation; 4) CKNN [22]: A hybrid recommendation method where the similarity between two users in CKNN is computed according to their weights in the category hierarchy; 5) SVD++ [23]: A model considers implicit feedback from users for rating prediction, and is considered as a state-of-the-art matrix factorization algorithm for recommendation; 6) HFT [24]: A method which combines basic latent factors in ratings with hidden topics in reviews. However, this approach uses ratings and reviews only; 7) TrustSVD [25]: A method built based on SVD++ by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user; 8) NRLRS [19]: A method models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting; 9) ConSVD [26]: A community-based collaborative filtering method for social recommender systems, which makes full use of the rich link/community structure within a social network.…”

Section: Integrating Users' Social Relationship Contextmentioning

confidence: 99%

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Exploiting Location-Based Context for POI Recommendation When Traveling to a New Region

Gao

Yang

et al. 2020

IEEE Access

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Traveling to a new region has become a very common thing for people, due to work or life requirement. With the development of recommendation engine and the popularity of social media network, people are more and more used to relying on personalized Points-of-Interest (POI) recommendations. However, traditional approaches can fail if users moves to a region where they had little or no active history or even social network friends information before. Under the requirement of smart city construction, the need to give high quality personalized POI recommendation when a user travels to a new region has arisen. Fortunately, with the widespread of wireless Internet, the booming of Internet-of-Things (IoT) and the common-usage of location sensors in mobile phones, the coupling degree between social media networks and location information is ever increasing, which could leads us to a new way to solve this problem in the ear of Big Data. In this research, we presented New Place Recommendation Algorithm (N-PRA) which is designed based on Latent Factor model. Many different types of social media contexts (time-related and location-related), such as a user's interest fluctuation, different types of POIs' popularity fluctuation, types of POIs, the influence of geographical neighborhood on POIs, and user's social network friendship are taken into consideration in this approach. The algorithm presented is verified on Yelp, an open-source real urban data-set, and compared against several other baseline POI recommendation algorithms. Experimental results show that the algorithm presented in this paper could achieve a better accuracy.

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Exploring IoT Location Information to Perform Point of Interest Recommendation Engine: Traveling to a New Geographical Region

Cited by 8 publications

References 40 publications

An Effective Hotel Recommendation System through Processing Heterogeneous Data

An Effective Hotel Recommendation System through Processing Heterogeneous Data

On-site Trip Planning Support System Based on Dynamic Information on Tourism Spots

Exploiting Location-Based Context for POI Recommendation When Traveling to a New Region

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