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Page 1: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based
Page 2: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

BackgroundInformation ExplosionPersonalization

2

Which book should I read?

Recommendation from Friends Auto Recommendation

Definition of Recommender Systems

Page 3: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

DefinitionRecommend the right content and products (items) to the right user at the right time so as to archive the most relevant experience.

DependenceMost Popular • “What’s most engaging overall?”

Personalized Recommendation• “What’s most relevant to me based on my interests

and attributes?”Related items• Behavioral Affinity: People who did X, did Y

Similarity: Based on metadata3

Definition of Recommender Systems

Page 4: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

A Case Study

4

Definition of Recommender Systems

Page 5: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Problem FormalizationWith Rating

Without Rating

5

Traditional Recommender System

Item-1 Item-2 Item-3 Item-4 Item-5

User-1 4 ? 2 5 ?

User-2 3 2 1 ? ?

User-3 ? 2 ? 3 ?

User-4 ? 3 3 5 4

Item-1 Item-2 Item-3 Item-4 Item-5

User-1 1 ? 1 1 ?

User-2 1 1 1 ? ?

User-3 ? 1 ? 1 ?

User-4 ? 1 1 1 1

Page 6: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Classification of AlgorithmsContent-based Method

6

Sequencepatternmining

Autoabstract

Textclassify

Webmining

Textmining

Machinelearning

Datamining

Socialnetwork Linux

Unix

C++Hello world

JavaHello world

NLU

NLP

Windows

WriteGood

papers

Searchpapers

ThinkingIn

C++

Traditional Recommender Systems

Page 7: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Classification of AlgorithmsCollaborative Filtering Method

User-based CF

7

Traditional Recommender Systems

new

Page 8: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Classification of AlgorithmsCollaborative Filtering Method

Item-based CF

8

Traditional Recommender Systems

new

Page 9: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Classification of Algorithms

Hybrid Methodimplementing collaborative and content-based methods separately and combining their predictions

incorporating some content-based characteristics into a collaborative approach

incorporating some collaborative characteristics into a content-based approach

constructing a general unifying model that incorporates both content-based and collaborative characteristics.

9

Traditional Recommender Systems

Page 10: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Motivation

Challenges for Mobile Recommendation

The Characteristics of Mobile DataTwo Case Studies• Location traces by taxi drivers• The tourism data

10

Mobile Recommender Systems

Page 11: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

MotivationBackground

Revolution in Mobile Devices• GPS• WiFi• Mobile phone

The Urgent Demand for Better Service• Driving route suggestion• Mobile tourist guides

DefinitionMobile Pervasive Recommendation is Promised to Provide Mobile Users Access to Personalized Recommendations Anytime, Anywhere.

11

Mobile Recommender Systems

Page 12: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Motivation

Traditional Recommender Systems don’t WorkThe complexity of spatial data and intrinsic spatio-temporal relationshipsThe unclear roles of context-aware informationThe lack of user rating informationThe diversified location-sensitive recommendation tasks

12

Mobile Recommender Systems

Page 13: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based
Page 14: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Challenges for Mobile Recommendation (I)

Complexity of the Mobile DataHeterogeneous

• The spatio-temporal data

Noisy

The Validation ProblemNo Ratings

The Transplantation ProblemDifficult to apply traditional Recommendation techniques for mobile recommendation

14

Mobile Recommender Systems

Page 15: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Challenges for Mobile Recommendation (II)

The Generality Problem Different application domains with different recommendation techniques

The Cost ConstraintsTimePrice

The Life Cycle Problem

15

Mobile Recommender Systems

Page 16: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

The Characteristics of Mobile Data

Two CasesCase 1. Location trace by taxi driversCase2. The tourism data

Why?A good coverage of unique characteristics of mobile dataCan be naturally exploited for developing mobile recommender systemsThey are the real-world data

16

Mobile Recommender Systems

Page 17: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

The Characteristics of Mobile DataCase 1. Location trace by taxi driversData Description

• GPS traces– Location information(Longitude, Latitude), timestamp– The operation status (with or without passengers)

17

Mobile Recommender Systems

Experienced drivers can usually have more driving hours and high occupancy rates

Inexperienced drivers tend to have less driving hours and low occupancy rates

Page 18: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Case 1. Location trace by taxi driversDriving pattern comparison

The Characteristics of Mobile Data

18

Mobile Recommender System

The experienced drivers have a wider operation area.

The experienced drivers know the roads as well the traffic patterns better.

Page 19: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

The Characteristics of Mobile Data

Case 1. Location trace by taxi driversWhat can we do?

• Develop a mobile recommender system– Users ~ Taxi drivers– Items ~ Potential pick-up points

What did we learn?• The difference between Mobile RS and traditional RS

– The items are application-dependent– There is some cost to extract items– The items are not i.i.d while spatial auto correlation

19

Mobile Recommender System

Page 20: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

The Characteristics of Mobile Data

Case2. The tourism dataData Description

• Expense records– Tourists: ID, travel time– Package: ID, name, landscapes, price, travel

days– Duration: 2000—2010

Recommender System• Users ~ Tourists• Items ~ Packages

20

Mobile Recommender Systems

Page 21: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

The Characteristics of Mobile DataCase2. The tourism dataCharacteristics of Tourism Data(I)

• Spatial auto correlation of packages– For example, the 1-day Niagara Falls Tour

• The Sparseness– Much sparser than the Netflix data set.

21

Mobile Recommender Systems

Page 22: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

The Characteristics of Mobile Data

Case2. The tourism dataCharacteristics of Tourism Data(II)

• The time dependence – Packages and tourists have seasonal tendency– Packages have a life cycle– Short-period packages are more popular

22

Mobile Recommender Systems

Page 23: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Introduction

Research MotivationTraditional recommender system• Prediction performance (MSE/RMSE)• Implicit/Explicit rating

Mobile recommender systems• Business success metrics• Location-based recommendation

23

An Energy-Efficient Mobile Recommender System

Page 24: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Mobile Sequential RecommendationProblem Formalization

24

An Energy-Efficient Mobile Recommender System

: all probabilities of all pick-up points contained in

PTD: Potential travel distance

Page 25: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Mobile Sequential Recommendation

25

An Energy-Efficient Mobile Recommender System

PoCab -> C1 -> C4 or PoCab -> C4 ->C3 -> C2?

An Example

Page 26: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

Definition of Recommender Systems

Traditional Recommender Systems

Mobile Recommender Systems

A Case Study for Mobile RS---An Energy-Efficient Mobile Recommender System

26

Summary

Page 27: Definition of Recommender Systems Background · Personalized Recommendation • “What’s most relevant to me based on my interests ... Collaborative Filtering Method User-based

27

Yong Ge


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Recommender systems: Collaborative & Content-based Filtering Features

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Collaborative Filtering Recommender Systems - …schafer/publications/CF_AdaptiveWeb_2006.pdf · Collaborative Filtering Recommender Systems J. Ben Schafer, Dan Frankowski, Jon Herlocker,

Recommender Systems TIETS43people.uta.fi/~kostas.stefanidis/docs/recsys17/lecture01... · 2018-10-22 · User-based Collaborative Filtering Make suggestions based on preferences of

Evaluating Collaborative Filtering Recommender Systems

Collaborative Filtering - University of Pittsburghpeterb/2480-132/CollaborativeFiltering.pdfCollaborative Filtering Recommender System " “Word-of-Mouth” phenomenon. ! Content-based

Recommender Systems: Advances in Collaborative Filtering

Web Personalization & Recommender Systemspeople.cs.georgetown.edu/~nazli/classes/ir-Slides/... · Item-based Collaborative Filtering i Find similarities among the items based on ratings

Personalized Recommender System Using Entropy Based Collaborative Filtering Technique · 2011-08-29 · Chandrashekhar & Bhasker: Entropy Based Collaborative Filtering Recommender

Contribution to Collaborative Filtering Based on Soft ...Contribution to Collaborative Filtering Based on Soft Computing to Enhance Recommender System for e-Commerce . Saad M. Darwish1*,

9 Collaborative Filtering Recommender Systemsricci/ISR/papers/cf-adaptive-web.pdf · 9 Collaborative Filtering Recommender Systems 293 (Table 1). Without loss of generality, a ratings

A Novel Collaborative - Filtering - Based Recommender ... 2013/Volume1/IJREATV1I1166.pdf · recommender systems, such as collaborative-filtering-based methods, content-based approaches

RECOMMENDER SYSTEM: COLLABORATIVE FILTERING OF E … · 2018-12-07 · RECOMMENDER SYSTEM: COLLABORATIVE FILTERING OF E-LEARNING RESOURCES . Baba Mbaye . Laboratory of ELLIADD, University

Filtering and Recommender Systems Content-based and Collaborative

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Selecting and Weighting Users in Collaborative Filtering ...acta.uni-obuda.hu/Rios_Schiaffino_Godoy_74.pdf · Keywords: Location-based social networks; recommender systems; user-based

Survey on Recommender Systemslyle.smu.edu/~mhd/8337sp11/Aradhya.pdf · which are – collaborative filtering approach, content‐based (or knowledge‐based) approach and hybrid approach

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Merging Interactive Information Filtering and Recommender ... · Merging Interactive Information Filtering and Recommender Algorithms – Model and oncept Demonstrator Abstract: and

The Use of Collaborative Filtering, Content based Filtering ...customers t recommender systems provide a scalable way of personalizing content for users in scenarios with many items

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[Final]collaborative filtering and recommender systems

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