Making recommendations bandwidth aware

Abstract

This paper asks how much we can gain in terms of bandwidth and user satisfaction, if recommender systems became bandwidth aware and took into account not only the user preferences, but also the fact that they may need to serve these users under bandwidth constraints, as is the case over wireless networks. We formulate this as a new problem in the context of index coding: we relax the index coding requirements to capture scenarios where each client has preferences associated with messages.The client is satisfied to receive any message she does not already have, with a satisfaction proportional to her preference for that message. We consistently find, over a number of scenarios we sample, that although the optimization problems are in general NP-hard, significant bandwidth savings are possible even when restricted to polynomial time algorithms.

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

“…In the bandwidth-aware recommendation problems, we consistently found that although the optimization problems are in general Non-deterministic Polynomal (NP)-hard, significant bandwidth savings are possible even when restricted to polynomial time algorithms [102] . We also found that there is a tradeoff between user preference and bandwidth usage, as shown in Fig.…”

“…In stark contrast to this, in Ref. [102], we considered to jointly optimize the user preference and data transmission efficiency, as it is the case over IoV networks.…”

“…5 Trade-off between bandwidth and benefit (user preference) in Ref. [102], where the bandwidth is described by the number of transmissions (horizontal axis); and the benefits are aggregated by summing up all users preferences (vertical axis). The three arrows in the middle point to the three points of the red line, representing the best, average, and worst cases obtained after multiple simulations.…”

MDP-based Network Friendly Recommendations

“…Ignoring network costs in content recommendation algorithms will inevitably lead to largely suboptimal performance for all parties involved. A handful of recent works have spotted the interplay between recommendationnetwork vs QoS-cost, and have proposed to modify the recommendation algorithms towards a more network-friendly operation [10,17,23,28,30,35,37]. The main objective of almost all these works can be summarized as how to recommend content that is still highly interesting to the user while at the same time its delivery cost is reduced.…”

User-centric Optimization of Caching and Recommendations in Edge Cache Networks

“…Some recent works study the problem of optimizing caching and/or recommendation policies by taking into account their interplay [4]- [9]. However, many of these works still focus on one side of the problem, e.g., caching-friendly recommendations [5], [9], or recommendation-aware caching policies [6]. Works that do try to control both the caching and recommendation policies are usually based on heuristics [7], [8].…”