items - The 1st Workshop on User Engagement Optimization at

76 Pages·2013·3.6 MB·English

by  Deepak Agarwal

Recommending Items to Users: An Explore Exploit
Perspective
Deepak Agarwal, Director Machine Learning and
Relevance Science, LinkedIn, USA

CIKM, 2013
Disclaimer
 Opinions expressed are mine and in no way
represent the official position of LinkedIn

 Material inspired by work done at LinkedIn and
Yahoo!
Main Collaborators: several others at both Y! and
LinkedIn
 I won’t be here without them, extremely lucky to work with such
talented individuals

Bo Long
Bee-Chung Chen Liang Zhang
Jonathan Traupman
Item Recommendation problem

Arises in both advertising and content

Serve the “best” items (in different

contexts) to users in an automated
fashion to optimize long-term
business objectives
Business Objectives
User engagement, Revenue,…
LinkedIn Today: Content Module
Objective: Serve content to maximize engagement
metrics like CTR (or weighted CTR)
Similar problem:
Content recommendation on Yahoo! front page

Today module
Recommend content links
F1
F2 F3 F4
(out of 30-40, editorially
programmed)

4 slots exposed, F1 has
maximum exposure

Routes traffic to other Y!
properties
LinkedIn Ads: Match ads to users visiting LinkedIn
Right Media Ad Exchange: Unified Marketplace
Bids $0.75 via Network…
Bids $0.50
Bids $0.60
Ad.com
AdSense
Bids $0.65—WINS!
Has ad
impression
to sell --
… which becomes
AUCTIONS
$0.45 bid
Match ads to page views on publisher sites
High level picture
Item
Recommendation
Server
system: thousands
http request
of computations in
sub-seconds
User Interacts Machine Learning
e.g. click,
Models Updated in
does nothing
Batch mode: e.g. once every
30 mins
High level overview: Item Recommendation System

Updated in batch:
Activity, profile User Info
ML/
Pre-filter Item Index
Score Items
SPAM,editorial,,.. Statistical
Id, meta-data
Feature extraction
P(Click), P(share),
Models
NLP, cllustering,..
Semantic-relevance
score,….
User-item interaction
Data: batch process
Rank Items:
sort by score (CTR,bid*CTR,..)
combine scores using Multi-obj optim,
Threshold on some scores,….