---
# System prepended metadata

title: TV3 <-> PICAE
tags: [PICAE]

---

---
title: TV3 <-> PICAE
tags: PICAE
slideOptions:
  theme: white
  transition: 'fade'
---

# 2 embeddings $(\mathbf u^{(1)}, \mathbf u^{(2)})$ per usuari

**Model independent**:


$$r_{ij} = b_0 + b_{\mathbf u^{(1)}} + b_{\mathbf u^{(2)}} + b_{\mathbf i} + b_4 \mathbf u^{(1)} \mathbf i + b_5 \mathbf u^{(2)} \mathbf i$$

on $b_4$ i $b_5$ són variables binàries que indiquen la popularitat de l'ítem.

**Model dependent**:

$$r_{ij} = b_0 + b_{\mathbf u^{(1)}} + b_{\mathbf u^{(2)}} + b_{\mathbf i} + b_4 \mathbf u^{(1)} \mathbf i + (1 - b_4) \mathbf u^{(2)} \mathbf i$$

on $b_4$ depen de la popularitat $p_i$ de l'ítem: $b 4 = sigmoid(NN_+(p_{\mathbf i}))$.

**Model amb "popularity as a mediator"**:

$$r_{ij} = sigmoid(c_i \times softplus(pr_{ij}))$$

on $pr_{ij} =  b_0 + b_{\mathbf u} + b_{\mathbf i} + \mathbf u \mathbf i$ 




# Avaluació recomanador de TV3 actual.

![](https://i.imgur.com/wSQiYQT.png)
![](https://i.imgur.com/I3i5B0N.png)

Com comparem dos recomanadors a partir de dades d'un? 
Counterfactual Learning and Evaluation for Recommender Systems: https://drive.google.com/file/d/1ZXWT8Y0ebjsTzzChzWuKVEUkUWVSkIrG/view

---



![](https://i.imgur.com/kz5dmhu.png)

The first stage is moderation, in which undesirable items are removed from the pool or flagged for special treatment.

In the candidate generation stage the full set of items available on the platform (potentially millions) is efficiently filtered to a set of 500 or so that are plausibly of interest to the user.

In the ranking stage each item is assigned a number intended to capture the value of showing it to a particular user in a particular context. Every recommender serves multiple stakeholders, so this will be some combination of value to the user, the content provider, the platform, and society. 

So far, this approach to ranking does not take into account the relationships between items in the ranked list — the position of each item is determined independently of the others. This can lead to the top-ranked items being too homogeneous. 

The re-ranking stage might aim to position complementary items near one another, prevent boredom by improving diversity in the item topics, promote fairness by improving diversity in the accounts presented, or counter popularity bias.

![](https://i.imgur.com/2cT264L.png)




## Les dades

+ Dades de la matriu col·laborativa: periode temporal, filtres aplicats, #usuaris, #programes. 
+ Com es tracten els logats i les cookies? Com es tracten els no logats?

## Mètriques

+ Com es mesura l'eficiència actualment?
+ Com es voldria mesurar?


## Comparativa Offline

+ L'objectiu es fer una comparativa "qualitativa":
    + Intersecció entre llistes recomanacio TV3-PICAE
    + Comparació de rankings TV3-PICAE
+ Necessitariem un llistat diari amb el conjunt de recomanacions efectuats? [`data`, `usuari`, `llista recomanació`] 


## Comparativa Online

+ Es poden recollir llistes de recomanació + clicks sobre aquesta llista?
+ Com organitzar un AB Test per comparar el funcionament online dels dos recomanadors amb una mètrica compartida.