recommender systems tiets43people.uta.fi/~kostas.stefanidis/docs/recsys17/lecture01... ·...
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Kostas [email protected]
Fall 2018
https://coursepages.uta.fi/tiets43/
Recommender Systems
TIETS43
Introduction
selection
recommendations
Amazon generates 35% of their sales through
recommendations
recommendations
selection
recommendations
selection
….
: internet radio
: video streaming, online DVD, Blu-ray Disc rental
: image organizer, image viewer
Recommender Systems
Recommender systems aim at suggesting to users items of potential interest to them
Two main steps: • Estimate a rating for each item and user • Recommend to the user the item(s) with the highest rating(s)
Why recommendations?
Why recommendations?
o Customer/user • Find interesting products/things to consume
• Narrow down the set of choices • Suggest additional things • Help exploring the space of options • Discover new things • …
o Seller/provider/generator • Personalized service for the user • Increase trust • Improve customer loyalty
• Increase sales • Opportunities for promotion, persuasion
• Obtain knowledge about customers • …
Purpose and success criteria
o Different perspectives/aspects • Depends on domain and purpose • No holistic evaluation scenario exists
o Retrieval perspective • Reduce search costs • Provide "correct" proposals • Users know in advance what they want
o Recommendation perspective • Serendipity – identify items from the Long Tail • Users did not know about existence
When does a RS do its job well?
▪ "Recommend widely unknown items that users might actually like!"
▪ 20% of items accumulate 74% of all positive ratings
Recommend items from the long tail
Purpose and success criteria
o Prediction perspective • Predict to what degree users like an item • Most popular evaluation scenario in research
o Interaction perspective • Give users a "good feeling" • Educate users about the product domain • Convince/persuade users - explain
o Conversion perspective • Commercial situations • Increase "hit", "clickthrough", "lookers to bookers" rates • Optimize sales margins and profit
The General Picture
product score
X4 0.8X3 0.6X1 0.2X5 0.1X2 0
Recommendations Generator
Recommender systems for estimating relevance
The General Picture
product score
X4 0.8X3 0.6X1 0.2X5 0.1X2 0
Recommendations Generator
Collaborative filtering: “ask my friends about the items they like”…
friends data
The General Picture
product score
X4 0.8X3 0.6X1 0.2X5 0.1X2 0
Recommendations Generator
Content-based: “show me items similar to those I previously preferred”description price …
… … …
items data
The General Picture
product score
X4 0.8X3 0.6X1 0.2X5 0.1X2 0
Recommendations Generator
Personalization
user profile
The General Picture
product score
X4 0.8X3 0.6X1 0.2X5 0.1X2 0
Recommendations Generator
Contextualization
user context
The General Picture
product score
X4 0.8X3 0.6X1 0.2X5 0.1X2 0
Recommendations Generator
Combine different mechanisms…
friends data
description price …
… … …
items data
user profile user context
Two main techniques: o Collaborative filtering o Content-based recommendations
Collaborative Filtering
Word of mouth! Use the wisdom of the crowd!
Produce interesting suggestions for a user (filtering) by using the taste of other users (collaboration)
To make suggestions/predict missing ratings, use: • Similar users - user-based collaborative filtering • Similar items - item-based collaborative
filtering
Assumption: • Users who had similar tastes in the past, will
have similar tastes in the future
X4
X4
X1
X1
X2
X3
User-based Collaborative Filtering
Make suggestions based on preferences of similar users o Given a user, identify his/her k most similar users
• Cosine similarity, Jaccard similarity o Produce recommendations based on the items that are liked by those k users
• avg ratings, weighted schemes
Expensive online computations
Item-based Collaborative Filtering
Exploit relationships between items o Compute similarities between items
• Cosine similarity, Jaccard similarity o Keep for each item only the k most similar items along with their similarity
scores o Use similarities to calculate ratings for items with no scores
Other techniques cluster users and recommend items the users in the cluster closest to the active user like
Back to this, in the context of group recommendations
Content-based Recommendations
o Analyze data information about items (docs, music, etc.)
o Extract features for items (actors, genre, ect.)
o Recommend items with features similar to items a user likes
Cold Start Problem: An all-time classic problem
o What happens with new users where we have no ratings yet? • Recommend popular items • Have some start-up questions (e.g., “provide 10 restaurants you love”)
o What happens with new items? • Content-based filtering techniques • Pay a set of users/customers to rate them (crowdsourcing)
Topics: o Collaborative Filtering o Content-based Filtering o Knowledge-based Recommendations o Hybrid Strategies
o Contextual Recommendations o Recommendations for Groups o Packages Recommendations o Explanations in Recommender Systems o Diversity in Recommender Systems o Fairness in Recommender Systems o Interactive Data Exploration
Structure
Modes of study o Lectures, exercises, student presentations in class Evaluation o Numeric 1-5 Course Work and Assessment o Assignments (3) (30%): Exercise problems on the recommendation models
studied, and short-answer questions on the papers and topics discussed in class
o Participation (10%): Participation in the class o Project (60%)
• The project will consist of the design and implementation of an innovative prototype for a recommender system in a specific application scenario, selected by the students (create groups of 2)
• The project will be accompanied with a short paper (7-8 pages long), describing the proposed ideas
• The project will be evaluated at the end of the period • Extra points will be given to students with an exceptionally good project
Project Topics
Feel free to propose your own topic! o Recommendations based on Linked (Open) Data o Entity-based Recommendations in Knowledge Graphs o Recommendations based on User Reviews o Interactive Recommendation o Cross-domain Recommendations o Natural Language Explanations for Recommendations o Chart-like Explanations for Recommendations o Diversity-aware Recommendations o Fairness-aware Group Recommendations
Project Topics
Feel free to propose your own topic! o Recommending Personalized News o Recommendation of Representative Reviews in e-Commerce o Recommending Product Packets to Customers o Insurance Recommendation Systems o Recommendations in the Health Domain o Points of Interest Recommendations o Package Recommendations for Trip Planning Activities o Travel Route Recommendations o Presentation of Recommendations for Hotels o Course Recommendations o Query-based Music Recommendations
Questions?