Using location for personalized POI recommendations in mobile environments

Horozov, T.; Narasimhan, Nitya; Vasudevan, Venu

doi:10.1109/saint.2006.55

Cited by 191 publications

(82 citation statements)

References 9 publications

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“…For example, users' personal characteristics such as age, gender, and cuisine preferences were used in [21], and social affinity was considered in [22,23]. User's history of online activity can also be collected, for example, search history; history of map browsing and spatial searching logs [24][25][26], place reviews and ratings [27][28][29], as well as explicit interaction on LBSN, by tagging and commenting on places [30,31]. In this work, users' location tracks are considered as the primary source of user-place relationships, as these represent explicit interaction with geographic places, normally recording actual visits to places.…”

Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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Knowledge of users' visits to places is one of the keys to understanding their interest in places. Usercontributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users' association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

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Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

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“…Most content-based recommender systems use relatively simple retrieval models, such as keywo rd matching or the Vector Space Model (VSM ) with basic TF-IDF weighting [15]. VSM is a spatial representation of text documents.…”

Section: Content-ba Sed Agentmentioning

confidence: 99%

An Agent Framework of Tourism Recommender System

Jia

Gao

Shi

2016

MATEC Web of Conferences

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Abstract. T his paper proposes the development of an Agent framework for tourism recommender system. T he recommender system can be featured as an online web application which is capable of generating a personalized list of preference attractions for tourists. T raditional technologies of classical recommender system application domains, such as collaborative filtering, content -based filtering and content-based filtering are effectively adopted in the framework. In the framework they are constructed as Agent s that can generate recommendations respectively. Recommender Agent can generate recommender information by integrating the recommendations of Content -based Agent, collaborative filtering-based Agent and constraint-based Agent. In order to make the performance more effective, linear combination method of data fusion is applied. User interface is provided by the tourist Agent in form of webpa ges and mobile app.

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“…In the collaborative filtering approach adopted by H o r o z o v, N a r a s i m h a n and V a s u d e v a n [3], the system is dealing with the cold start problem assuming that users who live close to each other like similar attractions; so the system introduced virtual user, sorted and rated the restaurants in advance, and this way it could find the same type of users with rare evaluation. The system could randomly generate option based on similarity drive and recommend user the nearest object to him with the highest average rating.…”

Section: Related Workmentioning

confidence: 99%