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LOCAl: a personalized cache mechanism for location-based social networks
Abstract
Recommending nearby Points of Interest (POI) has received growing interest in mobile location-based networks today, where users share content embedded with location information. In this work, we propose a novel caching framework to support personalised proactive caching for mobile location-based social networks. We propose "LOCAI", which uses a probabilistic approach in order to predict the POIs that users will access and retrieve the appropriate data objects that will fulfill user preferences. Our detailed experimental evaluation, using data from the Foursquare location-based social network, illustrates that LOCAI minimizes the user latency to retrieve the data objects they are interested in, is efficient and practical.
The widespread applications of mobile multimedia are causing the demands of the users on the network to be increasingly prominent. Edge computing enables the deployment of services, applications, content storage, and processing in close proximity to mobile end users. By fully exploiting its advantages, we propose the edge Internet of Things equipment-assisted caching multimedia for information centric networking for improving the user experience. To achieve this, we propose the location prediction method and smart caching strategy based on machine learning to predict the user interest in this paper. This will drive the user interest content from the server to the edge node. Moreover, we propose an optimized caching replacement algorithm for improving the cache utilization. The experimental results reveal that the proposed architecture and strategy are efficient for caching mobile multimedia content, which can optimize the hit ratio and reduce the access time in comparison with other existing solutions.
The availability of user check-in data in large volume from the rapid growing location-based social networks (LBSNs) enables a number of important location-aware services. Point-of-interest (POI) recommendation is one of such services, which is to recommend POIs that users have not visited before. It has been observed that: (i) users tend to visit nearby places, and (ii) users tend to visit different places in different time slots, and in the same time slot, users tend to periodically visit the same places. For example, users usually visit a restaurant during lunch hours, and visit a pub at night. In this paper, we focus on the problem of time-aware POI recommendation, which aims at recommending a list of POIs for a user to visit at a given time. To exploit both geographical and temporal influences in time-aware POI recommendation, we propose the Geographical-Temporal influences Aware Graph (GTAG) to model check-in records, geographical influence and temporal influence. For effective and efficient recommendation based on GTAG, we develop a preference propagation algorithm named Breadth-first Preference Propagation (BPP). The algorithm follows a relaxed breath-first search strategy, and returns recommendation results within at most 6 propagation steps. Our experimental results on two real-world datasets show that the proposed graph-based approach outperforms state-of-the-art POI recommendation methods substantially.
With the rapid growth of location-based social networks , Point of Interest (POI) recommendation has become an important research problem. However, the scarcity of the check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about POIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges. In the proposed model, we consider that the check-in frequency characterizes users' visiting preference and learn the factorization by ranking the POIs correctly. In our model , POIs both with and without check-ins will contribute to learning the ranking and thus the data sparsity problem can be alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a stochastic gradient descent based algorithm to learn the fac-torization. Experiments on publicly available datasets under both user-POI setting and user-time-POI setting have been conducted to test the effectiveness of the proposed method. Experimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
With the recent surge of location based social networks (LBSNs), activity data of millions of users has become attainable. This data contains not only spatial and temporal stamps of user activity, but also its semantic information. LBSNs can help to understand mobile users' spatial temporal activity preference (STAP), which can enable a wide range of ubiquitous applications, such as personalized context-aware location recommendation and group-oriented advertisement. However, modeling such user-specific STAP needs to tackle high-dimensional data, i.e., user-location-time-activity quadruples, which is complicated and usually suffers from a data sparsity problem. In order to address this problem, we propose a STAP model. It first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference. In order to characterize the impact of spatial features on user activity preference, we propose the notion of personal functional region and related parameters to model and infer user spatial activity preference. In order to model the user temporal activity preference with sparse user activity data in LBSNs, we propose to exploit the temporal activity similarity among different users and apply nonnegative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion framework to combine the spatial and temporal activity preference models for preference inference. We evaluate our proposed approach on three real-world datasets collected from New York and Tokyo, and show that our STAP model consistently outperforms the baseline approaches in various settings.
Providing location recommendations becomes an important feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical influence and social influence have been intensively used in location recommendations based on the facts that geographical proximity of locations significantly affects users' check-in behaviors and social friends often have common interests. Although human movement exhibits sequential patterns, most current studies on location recommendations do not consider any sequential influence of locations on users' check-in behaviors. In this paper, we propose a new approach called LORE to exploit sequential influence on location recommendations. First, LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L²TG). LORE then predicts the probability of a user visiting a location by Additive Markov Chain (AMC) with L²TG. Finally, LORE fuses sequential influence with geographical influence and social influence into a unified recommendation framework; in particular the geographical influence is modeled as two-dimensional check-in probability distributions rather than one-dimensional distance probability distributions in existing works. We conduct a comprehensive performance evaluation for LORE using two large-scale real data sets collected from Foursquare and Gowalla. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques.
With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an important research problem. However, the scarcity of the check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about POIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges. In the proposed model, we consider that the check-in frequency characterizes users' visiting preference and learn the factorization by ranking the POIs correctly. In our model, POIs both with and without check-ins will contribute to learning the ranking and thus the data sparsity problem can be alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a stochastic gradient descent based algorithm to learn the factorization. Experiments on publicly available datasets under both user-POI setting and user-time-POI setting have been conducted to test the effectiveness of the proposed method. Experimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
Sharing one taxi by more than one person is treated promising, since it enables us to take a taxi in rush-hour more conveniently. Hence, we develop POP, a prototype system to find appropriate partners to share a taxi with a given passenger. The framework of POP includes two phases, namely offline preprocessing and online matching. During the offline preprocessing phase, it constructs an R-Tree index for road network to speedup data access and computes average travel time for each road segment based on history trajectory data, while during the online matching, it tries to find appropriate partners to a given passenger which aims to save time as much as possible. We also propose a simple pricing method to allocate fee between passengers.
In this paper, we explore the problem of supporting efficient access to social media contents on social network sites for mobile devices without requiring mobile users to be online all the time. We propose and implement a broker/proxy based architecture that stages data at a broker/proxy, and selectively downloads to the mobile device only those contents that have a high likelihood of being viewed. The system determines the relevance of social media updates that continuously arrive (e.g., Facebook friend updates) for each user. Using knowledge of this relevance and current network/system conditions, we develop scheduling algorithms that determine which social contents are sent to the devices. We develop an Android app providing offline access to Facebook. Our experimental results indicate that our system is energy efficient, which saves energy by 6.9 times for WiFi and 9.1 times for cellular connections. We also use data traces gathered from our app to further drive extensive simulation based evaluations which show that our proposed algorithms provide efficient facilities for tuning the system's performance.
Mobile cloud computing, promising to extend the capabilities of resource-constrained mobile devices, is emerging as a new computing paradigm which has fostered a wide range of exciting applications. In this new paradigm, efficient data transmission between the cloud and mobile devices becomes essential. This, however, is highly unreliable and unpredictable due to several uncontrollable factors, particularly the instability and intermittency of wireless connections, fluctuation of communication bandwidth, and user mobility. Consequently, this puts a heavy burden on the energy consumption of mobile devices. Confirmed by our experiments, significantly more energy is consumed during “bad” connectivity. Inspired by the feasibility to schedule data transmissions for prefetching-friendly or delay-tolerant applications, in this paper, we present eTime, a novel Energy-efficient data Transmission strategy between cloud and Mobile dEvices, based on Lyapunov optimization. It aggressively and adaptively seizes the timing of good connectivity to prefetch frequently used data while deferring delay-tolerant data in bad connectivity. To cope with the randomness and unpredictability of wireless connectivity, eTime only relies on the current status information to make a global energy-delay tradeoff decision. Our evaluations from both trace-driven simulation and realworld implementation show that eTime can be applied to various popular applications while achieving 20%-35% energy saving.
Location-based services (LBS) have gained tremendous popularity with millions of simultaneous users daily. LBS handle very large data volumes and face enormous query loads. Both the data and the queries possess high locality: spatial data is distributed very unevenly around the globe, query load is different throughout the day, and users often search for similar things in the same places. This causes high load peaks at the data tier of LBS, which may seriously degrade performance. To cope with these load peaks, we present DiSCO, a distributed semantic cache overlay for LBS. DiSCO exploits the spatial, temporal and semantic locality in the queries of LBS and distributes frequently accessed data over many nodes. Based on the Content-Addressable Network (CAN) peer-to-peer approach, DiSCO achieves high scalability by partitioning data using spatial proximity. Our evaluation shows that DiSCO significantly reduces queries to the underlying data tier.
DiSCO: A Distributed Semantic Cache Overlay for Location-based Services
- Carlos Lübbe
Carlos Lübbe et al. "DiSCO: A Distributed Semantic
Cache Overlay for Location-based Services". In: MDM.
Vol. 1. IEEE. Lulea,Sweden, June 2011, pp. 17-26.