CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices

Ramaswamy, Lakshmish; Deepak, P; Polavarapu, Ramana; Gunasekera, Kutila; Garg, Dinesh; Visweswariah, Karthik; Kalyanaraman, Shivkumar

doi:10.1109/mdm.2009.66

Cited by 47 publications

(26 citation statements)

References 8 publications

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“…For example, [11] matches user profile data (e.g., gender, income and age) with the price and category of a restaurant using a Bayesian network model. Others took into consideration the proximity of the candidate location [12] or supported low-end devices [13] by focusing on using the user address and social affinity. In [14][15][16], the authors tried to explore spatial and temporal relations between locations within trajectories by extracting features and identifying correlations and sub-sequences related to the user's preferred activities.…”

Section: Related Workmentioning

confidence: 99%

Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media

Albanna

Sakr

Moussa

et al. 2016

IJGI

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Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1) utilizing the geo-content in both LBSNs and SNs; (2) ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness.

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Section: Related Workmentioning

confidence: 99%

Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media

Albanna

Sakr

Moussa

et al. 2016

IJGI

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“…Some of these areas are, for example, the study of the variability in the users ratings [4], the users coverage of the dataset [5], information provided by external users or experts [6], the temporal dimension [7], [8], [9] and the use of spatialtemporal information [3]. Frequently there exist limited data to perform the mining process.…”

Section: Related Work 21 Collaborative Recommendationmentioning

confidence: 99%

“… Reliability of the recommendations made by the users [3]. There are malicious users ratings introduced in order to damage the performance of the system itself.…”

mentioning

confidence: 99%

Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

Díez

Chavarriaga

Campos

et al. 2010

Proceedings of the Workshop on Context-Aware Movie Recommendation

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In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.

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“…They range over various domains such as language learning [16,17], social recommender systems [18], and tour guiding [19][20][21]. Location context has been widely used by mobile applications [22,23], and other contextual elements are starting to become popular to provide more personalized functionality.…”

Section: Mobile Applicationsmentioning

confidence: 99%

“…Location context has been widely used by mobile applications [22,23], and other contextual elements are starting to become popular to provide more personalized functionality. For example, CAESAR [18] makes use of social networks. The context-aware browser [24] makes use of user preferences, location, and activity.…”

Section: Mobile Applicationsmentioning

confidence: 99%