Caching Placement with Recommendation Systems for Cache-Enabled Mobile Social Networks

Wang, Yanfeng; Ding, Mingyang; Chen, Zhiyong; Luo, Ling

doi:10.1109/lcomm.2017.2705695

Cited by 43 publications

(23 citation statements)

References 15 publications

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“…The output of the CF algorithm is a complete rating matrix R 4×4 c . Then, user activity levels can be calculated from (16). For example, the probability that user u 1 generates a request is P (u 1 ) = 0.25 as the total number of ratings given (not predicted) by user u 1 in 4 time slots is n 1 = 1.…”

Section: Parameter Calculationmentioning

confidence: 99%

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A Personalized Preference Learning Framework for Caching in Mobile Networks

Malik

Kim

et al. 2021

IEEE Trans. on Mobile Comput.

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This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm. Moreover, we analyze the corresponding level of the approximation that our greedy algorithm can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters. Index TermsCaching, collaborative filtering, learning, mobile network, personalized file preferences. ✦ INTRODUCTIONT He growing trend in mobile data traffic drives a need of a new wireless communication technology paradigm in which radios are capable of learning and decision making in order to autonomously determine the optimal system configurations. In this context, equipping the communication functionality with machine learning-based or data-driven algorithms has received a considerable attention both in academia as well as in industrial communities. Prior WorkCache-enabled (or content-centric) wireless systems are equipped with finite storage capacity, which restricts us from storing the entire content library at the local cache. In order to bring content objects closer to requesting users, deciding which content needs to be cached at which user's or helper's cache plays a crucial role on the overall performance of content-centric wirelss networks. This cache placement problem has recently attracted a wide attention. In general, the prior work on caching can be divided into two categories: caching at small-cell base stations (or helper nodes) [1], [2], [3], [4] and caching at users (or devices) [5], [6], [7], [8]. Since the optimal caching problems in [1], [2], [3], [4], [5], [6] cannot be solvable in polynomial time, approximate solutions with performance guarantees were presented. It was shown in [1],[5] that the optimal cache placement problem fell in the category of monotone submodular maximization over a matroid constraint [9], and a proposed time-efficie...

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Section: Parameter Calculationmentioning

confidence: 99%

“…On the other hand, the importance of personalized file preferences in content-centric networks was studied in [6], [14], [15], [16]. In [6], a low-complexity semigradient-based cooperative caching scheme was designed in mobile social networks by incorporating probabilistic modeling of user mobility and heterogeneous interest patterns.…”

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confidence: 99%

A Personalized Preference Learning Framework for Caching in Mobile Networks

Malik

Kim

et al. 2021

IEEE Trans. on Mobile Comput.

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“…Angreedy algorithm was proposed in [25] to learn the impact of recommendation on user requests via interactions with users, and then a joint recommendation and caching policy was optimized to maximize cache hit ratio. All these works that incorporating recommendation with caching do not consider user mobility since they assume that the user's location when sending request is fixed and known [18], [23]- [26], or the request probability of each user in each cell is known [22]. Besides, these works only optimize the policies for a single cache update period.…”

Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Recommendation for Caching at Base Stations via Deep Reinforcement Learning

Guo

Yang

2019

IEEE Access

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Proactive caching at the base station (BS) is a promising way to leverage the user-behaviorrelated information to boost network throughput and improve user experience. However, the gain of caching at the mobile edge highly depends on random user behavior and is largely compromised by the uncertainty in predicting behavior-related information. First, the local file popularity in each cell may not be skewed. Second, the local file popularity varies quickly due to user mobility even if the lifetime of each file is long. Furthermore, considering the small population of users that initiate requests in each cell, the local popularity in the next cache update period is not easy to predict accurately, because users may not request their interested files in this period, despite that the popularity can be indirectly obtained by predicting the mobility and preference of each individual user in a cell. To address such issue, in this paper, we integrate recommendation with caching at BS, aiming at improving cache efficiency whereas not violating user preference. In particular, we propose a temporal-spatial recommendation policy, which can guide mobile users to request their preferred files in proper time and place, so as to make local popularity peakier. We do not assume that the user preference, the impact of the recommendation on request probability, and the mobility pattern are known. Hence, we resort to deep reinforcement learning to optimize recommendation and caching policy. To deal with the difficulty in predicting local popularity in the next cache replacement period, we model the user preference and request probability with Bernoulli mixture distribution and hence can estimate them separately. The simulation results demonstrate that the proposed policy can reduce the cache miss number, compared to the policies without any recommendation and without temporal-spatial recommendation. INDEX TERMS Caching, recommendation, user preference, user mobility.

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“…However, their prefetching algorithm is designed for a single user, which does not reduce the network traffic, since duplicate videos still need to be transmitted from the content provider to the users. In [97], an important user is selected as the helper to cache the recommended contents which are generated by a RS in a mobile network. Other users can fetch their interested contents from the important user.…”

Section: Machine Learning For Proactive Cachingmentioning

confidence: 99%

“…networks [76]; by combing ICN and IoT, we can get ICN-IoT networks [1]. With the help of emerging technologies, such as software-defined networking (SDN) [45] and machine learning algorithms (e.g., recommender system algorithms [98]), the efficiency of innetwork caching can be further improved.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvements of In-Network Caching in ICN-Based Networks

Zhang¹

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Pursing my Ph.D. degree in Canada was an exciting and amazing journey for me, and it started when I wrote my first email to my supervisor, Prof. Chung-Horng Lung. Hence, I would like to begin this section by expressing my deeply heartfelt thanks and gratitude to him, not only for his wholehearted guidance on my life as a student, but also for his profound influence on my values as a human being. From him, I learned how to perform research with rigorousness, and how to treat others with patience. Without his encouragements and supports, I would not have such a wonderful study. I would also like to thank my co-supervisor, Prof. Marc St-Hilaire, for his valuable comments and guidance. The academic writing skills and research methodologies he taught me helped me during my entire Ph.D. study and will continue to benefit my future work. More importantly, his rigorous attitude to academic is the most valuable treasure that I learned from him. Special thanks to Prof. Ioannis Lambadaris for offering me the internship opportunity at Ericsson. Besides, his immense knowledge and kind suggestions not only improve the quality of the research but also broaden my mind for my future career. I would also like to thank the committee members, Prof. Amiya Nayak, and Prof. F. Richard Yu for their helpful and insightful questions and suggestions. Many thanks also go to my friends Andy Yin,

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Caching Placement with Recommendation Systems for Cache-Enabled Mobile Social Networks

Cited by 43 publications

References 15 publications

A Personalized Preference Learning Framework for Caching in Mobile Networks

A Personalized Preference Learning Framework for Caching in Mobile Networks

Temporal-Spatial Recommendation for Caching at Base Stations via Deep Reinforcement Learning

Performance Improvements of In-Network Caching in ICN-Based Networks

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