A pragmatic and industry-aware approach toward the design of on-line recommender systems

Paolo Cremonesi
Proceedings of the 2nd Workshop on Online Recommder Systems and User Modeling, PMLR 109:1-1, 2019.

Abstract

On-line recommender systems are designed to address a number of different recommendation scenarios in which traditional systems fail primarily, but not only, due to scalability issues. The goal of this talk is to give participants an overview on the design requirements for on-line recommender systems, with a focus on their quality evaluation, and to provide pragmatic guidelines to perform these activities more effectively avoiding commons pitfalls. The talk is structured into two parts. In the first part, after a general overview of on-line recommender systems, we will analyze different application scenarios. In the second part we will analyze possible functional and non-functional evaluation problems. We will present some of our works on evaluating presentation biases, problems which affect click-based on-line recommender systems. We will later present some of our recent work towards comparing the scalability of different Top-N recommender systems, understanding their fundamental limitations and characteristics for some of the application scenarios identified in the first part.

Cite this Paper

BibTeX
@InProceedings{pmlr-v109-cremonesi19a, title = {A pragmatic and industry-aware approach toward the design of on-line recommender systems}, author = {Cremonesi, Paolo}, booktitle = {Proceedings of the 2nd Workshop on Online Recommder Systems and User Modeling}, pages = {1--1}, year = {2019}, editor = {Vinagre, João and Jorge, Alípio Mário and Bifet, Albert and Al-Ghossein, Marie}, volume = {109}, series = {Proceedings of Machine Learning Research}, month = {19 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v109/cremonesi19a/cremonesi19a.pdf}, url = {https://proceedings.mlr.press/v109/cremonesi19a.html}, abstract = {On-line recommender systems are designed to address a number of different recommendation scenarios in which traditional systems fail primarily, but not only, due to scalability issues. The goal of this talk is to give participants an overview on the design requirements for on-line recommender systems, with a focus on their quality evaluation, and to provide pragmatic guidelines to perform these activities more effectively avoiding commons pitfalls. The talk is structured into two parts. In the first part, after a general overview of on-line recommender systems, we will analyze different application scenarios. In the second part we will analyze possible functional and non-functional evaluation problems. We will present some of our works on evaluating presentation biases, problems which affect click-based on-line recommender systems. We will later present some of our recent work towards comparing the scalability of different Top-N recommender systems, understanding their fundamental limitations and characteristics for some of the application scenarios identified in the first part.} }
Endnote
%0 Conference Paper %T A pragmatic and industry-aware approach toward the design of on-line recommender systems %A Paolo Cremonesi %B Proceedings of the 2nd Workshop on Online Recommder Systems and User Modeling %C Proceedings of Machine Learning Research %D 2019 %E João Vinagre %E Alípio Mário Jorge %E Albert Bifet %E Marie Al-Ghossein %F pmlr-v109-cremonesi19a %I PMLR %P 1--1 %U https://proceedings.mlr.press/v109/cremonesi19a.html %V 109 %X On-line recommender systems are designed to address a number of different recommendation scenarios in which traditional systems fail primarily, but not only, due to scalability issues. The goal of this talk is to give participants an overview on the design requirements for on-line recommender systems, with a focus on their quality evaluation, and to provide pragmatic guidelines to perform these activities more effectively avoiding commons pitfalls. The talk is structured into two parts. In the first part, after a general overview of on-line recommender systems, we will analyze different application scenarios. In the second part we will analyze possible functional and non-functional evaluation problems. We will present some of our works on evaluating presentation biases, problems which affect click-based on-line recommender systems. We will later present some of our recent work towards comparing the scalability of different Top-N recommender systems, understanding their fundamental limitations and characteristics for some of the application scenarios identified in the first part.
APA
Cremonesi, P.. (2019). A pragmatic and industry-aware approach toward the design of on-line recommender systems. Proceedings of the 2nd Workshop on Online Recommder Systems and User Modeling, in Proceedings of Machine Learning Research 109:1-1 Available from https://proceedings.mlr.press/v109/cremonesi19a.html.

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