Characterizing context-aware recommender systems: A systematic literature review

Villegas, Norha M.; Snchez, Cristian; Daz-Cely, Javier; Tamura, Gabriel

doi:10.1016/j.knosys.2017.11.003

Cited by 186 publications

(70 citation statements)

References 53 publications

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“…Traditional recommender systems (RSs) neglect the contextual conditions when providing recommendations. Context-aware recommender systems (CARs) are normally developed to recommend items that are relevant to altering user needs by incorporating contextual data into RSs [8,9]. The term "context" is commonly defined as "any information useful to characterize the situation of an entity (e.g., a user or an item) that can affect the way users interact with systems" [10].…”

Section: Context-aware Recommender Systemmentioning

confidence: 99%

“…The major challenge for CARSs is to determine when and how context information should be incorporated. There are three distinct paradigms for integrating contextual data into recommender systems based on the phase in which context is analyzed: contextual pre-filtering, contextual post-filtering and contextual modeling [8,9].…”

Section: Context-aware Recommender Systemmentioning

confidence: 99%

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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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Section: Context-aware Recommender Systemmentioning

confidence: 99%

Section: Context-aware Recommender Systemmentioning

confidence: 99%

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

Khazaei

Alimohammadi

2019

IJGI

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“…The earliest wearable fitness trackers debuted over a decade ago. Building on their foundation, researchers have applied these mobile technologies to cognitive and physical health monitoring [1][2][3][4], activity-aware recommendations [5], sports evaluation and training [6], lifelogging [7], and behavior intervention [8].…”

Section: Introductionmentioning

confidence: 99%

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Culman

Aminikhanghahi

Cook

2020

Sensors

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Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.

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“…This definition reduces places to geometric objects, and while not per se wrong, it effectively ignores the nuances presented above and treats places as objects represented in some entity-based model of space. For example, Villegas et al (2018) introduce the idea of location context, where a place is treated simply as a location:…”

Section: Introductionmentioning

confidence: 99%

“…the coordinates of the user's location, a movie theater's address, or the directions to reach the movie theater from the costumer's current location), and virtual (e.g. the IP address of a computer that is located within a network) (Villegas et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Approaching location-based services from a place-based perspective: from data to services?

Bahrehdar

Chesnokova

Purves

2019

Journal of Location Based Services

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Despite the seemingly obvious importance of a link between notions of place and the provision of context in locationbased services (LBS), truly place-based LBS remain rare. Place is attractive as a concept for designing services as it focuses on ways in which people, rather than machines, represent and talk about places. We review papers which have extracted place-relevant information from a variety of sources, examining their rationales, the data sources used, the characteristics of the data under study and the ways in which place is represented. Although the data sources used are subject to a wide range of biases, we find that existing methods and data sources are capable of extracting a wide range of place-related information. We suggest categories of LBS which could profit from such information, for example, by using place-related natural language (e.g. vernacular placenames) in tracking and routing services and moving the focus from geometry to place semantics in location-based retrieval. A key future challenge will be to integrate data derived from multiple sources if we are to advance from individual case studies focusing on a single aspect of place to services which can deal with multiple aspects of place.

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Characterizing context-aware recommender systems: A systematic literature review

Cited by 186 publications

References 53 publications

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

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

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Approaching location-based services from a place-based perspective: from data to services?

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