Random walk based context-aware activity recommendation for location based social networks

Bagci, Hakan; Karagöz, Pınar

doi:10.1109/dsaa.2015.7344852

Cited by 12 publications

(2 citation statements)

References 17 publications

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“…Bagci et al implemented a heuristic approach based on user-user similarity using a graph that combines the current context of the user and the social relationships deduced from a location-based social network (LBSN). This approach predicts the ratings associated with different tourist activities and recommends the best ones [22]. On the other hand, Mingxin Gan and Ling Gao have integrated the psychological effects and preferences of a user into the POI recommendation heuristic.…”

Section: Literature Reviewmentioning

confidence: 99%

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

HADJHENNI,

DENNOUNI,

SLAMA

2024

IJCDS

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Intelligent tourism can increase the interest of nomadic tourists in discovering new cities. However, many Points of Interest (POIs) are available, creating an information overload for tourists when choosing POIs to visit. For this reason, CARS (Context-Aware Recommendation Systems) can play an important role by exploiting the experiences of previous tourists and their contexts to recommend attractive POIs. Consequently, choosing the right POI recommendation algorithm (RA) for CARS is crucial because it involves the costly intervention of real tourists during the test phase. In order to make this phase more cost-effective, we can test several RAs simultaneously in order to assess their limitations in terms of cold start and tourist satisfaction. To compare these RAs, we propose in this article an approach called SEPRA (Systematic Evaluation for POI Recommendation Algorithms), which allows us to carry out an initial online evaluation of each tourist during their visit and a second offline evaluation of each CARS after the end of each POI path. To achieve this objective, we designed and implemented a new smart tourism tool that makes POI recommendations using two algorithms: the first is based on tourist/tourist similarity, and the second uses POI/POI similarity. These algorithms use memory-based collaborative filtering and are executed in parallel by our tool in the form of CARSs, incorporating time or weather as context variables. To evaluate these systems during their test phases, Our approach enables: (1) the calculation of prediction accuracy; (2) the examination of the relevance of the recommended POIs; and (3) the estimation of the acceptance rate of the recommendation process. Finally, the experimental results obtained with our approach show that the algorithm based on tourist similarity is more resistant to the cold start problem during the test phase and has a better satisfaction rate than the algorithm based on POI similarity.

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Section: Literature Reviewmentioning

confidence: 99%

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

HADJHENNI,

DENNOUNI,

SLAMA

2024

IJCDS

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“…RWR would not permit moving out of context by a constant probability of jumping back to the starting node in each move. Due to this limitation, nodes nearer to the starting node are likely to have more visits [21,22].…”

Section: Random Walk With Restartmentioning

confidence: 99%

Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Khazaei

Alimohammadi

2019

IJGI

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Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.

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A Survey on Location Recommendation Systems

Narvekar

Nayak

Bakal

2017

Communications in Computer and Information Science

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Random walk based context-aware activity recommendation for location based social networks

Cited by 12 publications

References 17 publications

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

A Survey on Location Recommendation Systems

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