Making recommendations bandwidth aware

TL;DR

This paper explores how bandwidth-aware recommender systems can optimize content delivery over wireless networks by incorporating user preferences and satisfaction, demonstrating significant bandwidth savings despite computational complexity.

Contribution

It introduces a new index coding problem that accounts for user preferences, showing potential for bandwidth efficiency in recommender systems under constraints.

Findings

Bandwidth savings are achievable with polynomial-time algorithms.

Significant improvements over traditional methods in various scenarios.

NP-hardness of the optimization problem does not preclude practical solutions.

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.