Ecole d'ingénieur et centre de recherche en Sciences du numérique

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

Giannakas,, Theodoros; Spyropoulos, Thrasyvoulos; Sermpezis, Pavlos

WIOPT 2019, International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, 3-7 June 2019, Avignon, France

--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 streamin g 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 ope n 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 stochasti c 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. 

Document Arxiv Bibtex

Titre:The order of things: Position-aware network-friendly recommendations in long viewing Sessions
Département:Systèmes de Communication
Eurecom ref:5887
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Bibtex: @inproceedings{EURECOM+5887, year = {2019}, title = {{T}he order of things: {P}osition-aware network-friendly recommendations in long viewing {S}essions}, author = {{G}iannakas,, {T}heodoros and {S}pyropoulos, {T}hrasyvoulos and {S}ermpezis, {P}avlos}, booktitle = {{WIOPT} 2019, {I}nternational {S}ymposium on {M}odeling and {O}ptimization in {M}obile, {A}d {H}oc, and {W}ireless {N}etworks, 3-7 {J}une 2019, {A}vignon, {F}rance }, address = {{A}vignon, {FRANCE}}, month = {06}, url = {} }
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