Caching-aware recommendations: Nudging user preferences towards better caching performance

Chatzieleftheriou, Livia Elena; Karaliopoulos, Merkourios; Koutsopoulos, Iordanis

doi:10.1109/infocom.2017.8057031

Cited by 96 publications

(100 citation statements)

References 21 publications

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“…In our experiments, we randomly select N users and F items out of 333 users and 261 items, respectively, in the pre-filtered rating matrix. We use the Pearson correlation in (15) to evaluate the similarity.…”

Section: Dataset and Pre-filteringmentioning

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%

“…Later, the optimal caching policy via a greedy algorithm for cache-enabled device-to-device (D2D) communications was presented in [14] by modeling the behavior of user requests resorting to probabilistic latent semantic analysis. In [15], when there is some coordination between recommender systems and caching decisions in a wireless network with small cells, possible benefits that can arise for the end users were investigated. In [16], a recommender system-aided caching placement scheme was also developed in wireless social networks.…”

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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: Dataset and Pre-filteringmentioning

confidence: 99%

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confidence: 99%

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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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“…Previously, this dataset has been used for recommendation system studies . Recently, dataset ML100K has been used for content caching in the cellular network . This dataset contains 100‐000 ratings between (1‐5) from 943 user on 1682 movies.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…41 Recently, dataset ML100K has been used for content caching in the cellular network. 51,52 This dataset contains 100-000 ratings between (1-5) from 943 user on 1682 movies. Here, each movie is considered as a proxy for the files f from |F| library.…”

Section: Mobility and Caching Datamentioning

confidence: 99%

Leveraging mobility and content caching for proactive load balancing in heterogeneous cellular networks

Manzoor

Mazhar

Asghar

et al. 2019

Trans Emerging Tel Tech

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Evolution of cellular networks into dynamic, dense, and heterogeneous networks have introduced new challenges for cell resource optimization, especially in the imbalanced traffic load regions. Numerous load balancing schemes have been proposed to tackle this issue; however, they operate in a reactive manner that confines their ability to meet the top‐notch quality of experience demands. To address this challenge, we propose a novel proactive load balancing scheme. Our framework learns users' mobility and demands statistics jointly to proactively cache future contents during their stay at lightly loaded cells, which results in quality of experience maximization and load minimization. System level simulations are performed and compared with the state‐of‐the‐art reactive schemes.

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Leveraging recommendation systems for improving caching emerging short video in content delivery network

2020

Trans Emerging Tel Tech

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Short video applications have rapidly become one of the most popular online entertainments. It is also one of the most important ways of social networking. However, the request pattern of short videos shows the characteristics of highly dynamic compared to traditional video. First, the access of short video is changing quickly, that is, user shows less preferences. Second, most short videos have no tag descriptions. These result in a significant reduction in caching efficiency of content delivery network (CDN) server, which is used to cache short videos. To address these challenges, this article proposes a recommendation system to optimize short video caching. For lack of tag description, this article proposes event detection method to recognize tags based on improved dense trajectory and concept dictionary video feature extraction. Then the collaborative filtering method is used to generate caching recommendation list for CDN to guide short video caching. Simulation results show that the proposed method can significantly improve the efficiency of short video caching for CDN.

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Caching-aware recommendations: Nudging user preferences towards better caching performance

Cited by 96 publications

References 21 publications

A Personalized Preference Learning Framework for Caching in Mobile Networks

A Personalized Preference Learning Framework for Caching in Mobile Networks

Leveraging mobility and content caching for proactive load balancing in heterogeneous cellular networks

Leveraging recommendation systems for improving caching emerging short video in content delivery network

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