Matrix Factorization Techniques for Recommender Systems

This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this special issue. We analyze the challenges that caching needs to address today, also considering an industry perspective, and identify bottleneck issues that must be resolved to unleash the full potential of this promising technique.

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The Role of Caching in Future Communication Systems and Networks

Pushing popular content to small cells with local storage (“helper” nodes) has been proposed to cope with the ever-growing data demand. Nevertheless, the collective storage of a few nearby helper nodes may not suffice to achieve a high hit rate in practice. In this paper, we introduce the concept of “soft cache hits” (SCHs). An SCH occurs if a user’s requested content is not in the local cache, but the user can be (partially) satisfied by a related content that is. In case of a cache miss, an application proxy (e.g., YouTube) running close to the helper node (e.g., at a multi-access edge computing server) can recommend the most related files that are locally cached. This system could be activated during periods of predicted congestion, or for selected users (e.g., low-cost plans), to improve cache hit ratio with limited (and tunable) user quality of experience performance impact. Beyond introducing a model for soft cache hits, our next contribution is to show that the optimal caching policy should be revisited when SCHs are allowed. In fact, we show that optimal caching with SCH is NP-hard even for a single cache . To this end, we formulate the optimal femto-caching problem with SCH in a sufficiently generic setup and propose efficient algorithms with provable performance. Finally, we use a large range of real datasets to corroborate our proposal.

Soft Cache Hits: Improving Performance Through Recommendation and Delivery of Related Content

Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.

Jointly Optimizing Content Caching and Recommendations in Small Cell Networks

Classification of optimization problems in fog computing

Pushing popular content to cheap ``helper'' nodes (e.g., small cells with local storage) during off-peak hours has recently been proposed to cope with the increase in mobile data traffic. If the requested content is available locally at a helper node, both user and operator performance could benefit. Nevertheless, the collective storage of a few nearby helper nodes does not usually suffice to achieve a high hit rate in practice. In this paper, we investigate the concept of ``soft cache hits'' where, if the original content is not available, some locally cached related contents can be recommended. Given that Internet content consumption is entertainment-oriented, we argue that there exist scenarios where a user might accept an alternative content (e.g., better download rate for alternative content, low rate plans), thus avoiding to access expensive/congested links. We formulate the problem of optimal edge caching with soft cache hits in a sufficiently generic setup, propose an efficient algorithm, and analyze the expected gains. We then show using synthetic and real datasets of related video contents that promising caching gains could be achieved in practice.

Femto-Caching with Soft Cache Hits: Improving Performance with Related Content Recommendation

Caching has been successfully applied in wired networks, in the context of Content Distribution Networks (CDNs), and is quickly gaining ground for wireless systems. Storing popular content at the edge of the network (e.g, at small cells) is seen as a “win-win” for both the user (reduced access latency) and the operator (reduced load on the transport network and core servers). Nevertheless, the much smaller size of such edge caches, and the volatility of user preferences suggest that standard caching methods do not suffice in this context. What is more, simple popularity-based models commonly used (e.g, IRM) are becoming outdated, as users often consume multiple contents in sequence (e.g. YouTube, Spotify), and this consumption is driven by recommendation systems. The latter presents a great opportunity to bias the recommender to minimize content access cost (e.g, maximizing cache hit rates). To this end, in this paper we first propose a Markovian model for recommendation-driven user requests. We then formulate the problem of biasing the recommendation algorithm to minimize access cost, while maintaining acceptable recommendation quality. We show that the problem is non-convex, and propose an iterative ADMM-based algorithm that outperforms existing schemes, and shows significant potential for performance improvement on real content datasets.

Show me the Cache: Optimizing Cache-Friendly Recommendations for Sequential Content Access

Caching popular content at the network edge can benefit both the operator and the client by alleviating the backhaul traffic and reducing access latency, respectively. Recommendation systems, on the other hand, try to offer interesting content to the user and impact her requests, but independently of the caching policy. Nevertheless, it has been recently proposed that designing caching and recommendation policies separately is suboptimal. Caching could benefit by knowing the recommender's actions in advance, and recommendation algorithms could try to favor cached content (among equally interesting options) to improve network performance and user experience. In this paper we tackle the problem of optimally making caching and recommendation decisions jointly, in the context of the recently introduced “soft cache hits” setup. We show that even the simplest (one user, one cache) problem is NP-hard, but that the most generic problem (multiple users, femtocaching network) is approximable to a constant. To the best of our knowledge, this is the first polynomial algorithm with approximation guarantees for the joint problem. Finally, we compare our algorithm to existing schemes using a range of real-world data-sets.

Approximation Guarantees for the Joint Optimization of Caching and Recommendation.

Caching has recently attracted a lot of attention in the wireless communications community, as a means to cope with the increasing number of users consuming web content from mobile devices. Caching offers an opportunity for a win-win scenario: nearby content can improve the video streaming experience for the user, and free up valuable network resources for the operator. At the same time, recent works have shown that recommendations of popular content apps are responsible for a significant percentage of users requests. As a result, some very recent works have considered how to nudge recommendations to facilitate the network (e.g., increase cache hit rates). In this paper, we follow up on this line of work, and consider the problem of designing cache friendly recommendations for long viewing sessions; specifically, we attempt to answer two open questions in this context: (i) given that recommendation position affects user click rates, what is the impact on the performance of such network-friendly recommender solutions? (ii) can the resulting optimization problems be solved efficiently, when considering both sequences of dependent accesses (e.g., YouTube) and position preference? To this end, we propose a stochastic model that incorporates position-aware recommendations into a Markovian traversal model of the content catalog, and derive the average cost of a user session using absorbing Markov chain theory. We then formulate the optimization problem, and after a careful sequence of equivalent transformations show that it has a linear program equivalent and thus can be solved efficiently. Finally, we use a range of real datasets we collected to investigate the impact of position preference in recommendations on the proposed optimal algorithm. Our results suggest more than 30% improvement with respect to state-of-the-art methods.

Theodoros Giannakas

Papers

Soft Cache Hits: Improving Performance Through Recommendation and Delivery of Related Content

Femto-Caching with Soft Cache Hits: Improving Performance with Related Content Recommendation

Show me the Cache: Optimizing Cache-Friendly Recommendations for Sequential Content Access

Approximation Guarantees for the Joint Optimization of Caching and Recommendation.

The order of things: Position-aware network-friendly recommendations in long viewing Sessions