toward the next generation of recommender systems 2008. 11.05 ieee transactions on knowledge and...

Toward the Next generation of Recommender systems

2008. 11.05IEEE Transactions on Knowledge and Data Engineering

Volume 17 , Issue 6 (June 2005)

Written by Gediminas Adomavicius, Alexander Tuzhilin

Summarized by Gihyun Gong

Copyright 2008 by CEBT

About paper

This paper is about an overview of recommendation sys-tem

Focused on rating based recommendation which is most popular

Content based

Collaborative filtering

Hybrid methods

Extending capabilities of recommendation system


Outline

About recommendation

Recommendation methods

Demographic filtering

Content-based Methods

Collaborative Methods

Hybrid Methods

Current research issues in recommendation system


Recommendation

Recommendation is type of information filtering technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user

Recommendation can be formulated as :

C : all users

S : set of all possible item

u : function that measures the usefulness of item s to user c

Recommendation is reduced to the problem ofestimating ratings for the items that have not been seen by a user

How to rating?

How to estimating?


Recommendation (cont’d)

Problem of recommender system

Usually not defined on the whole C X S space, but only on some subset of it

Recommendation engine should be able to estimate the ratings of the non-rated movie/user


Recommendation system

Recommendation system is a system which has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options

Recommender systems are usually classified into the following categories, based on how recommendations are made:


Content-based recommendations: The user will be recommended items similar to the ones the user preferred in the past

Collaborative recommendations: The user will be recommended items that are preferred by other people with similar tastes and preferences

Hybrid approaches: These methods combine collaborative and content-based methods.



Uses demographic information

Ages, Jobs, Location, …

Advantages

No feedback is needed

No cold start problem

Disadvantages

Can not provide personalization

Low accuracy

Too general


Content-based recommendation

Recommend items similar to those users preferred in the past

User preference profile is the key

Matching “user preferences” with “item characteris-tics”

Designed mostly to recommended text-based items

The content in these system is usually described with key-words

Similarity measure

TF-IDF

Cosine similarity


Similarity function

TF-IDF

N is the number of documents

Ni is How many times keyword ki is appears in the document

Fi,j is the number of times keyword ki is appears in the document j

Cosine Similarity

For text matching, the attribute vectors A and B are usually the tf-idf vectors of the documents.

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Limitation of Content-based method

Limited Content Analysis

This method is based on text, but not all content is well repre-sented by keywords

– Picture, Taste, …

Overspecialization

User is limited to being recommended items already rated

Unrated items not shown

Use random or mutation in genetic algorithm to solve

New User Problem

This method uses user preference profile

New user have very few ratings (or no history available)

System needs new user’s rating of sample items

However, people usually do not want to rate sample items


Collaborative Filtering

Using Trend information, 『Word of Mouth』 Basic idea of CF

1. Build a ratings table from user rating.

2. Compare user’s ratings, and calculate similarity between users.We call the user group which presents high similarity that ‘Nearest Neighborhood’

3. Predict user preference based on rating of Nearest neighbor-hood.


Collaborative Filtering methods

Memory-based (or Nearest-Neighborhood)

Similarity based model

Use entire collection of previously rate item by the user

Store all user information in a Database

Model-based

Probabilistic model

Use collection of rating to learn a model, which is used to make rating prediction

Based on machine-learning

– Bayesian network, Clustering, NN, …


Advantages of Collaborative Fil-tering

Can deal with multimedia contents

Can recommend based on user preference and quality of item

Can recommend serendipity item


Limitation of Collaborative method

New User Problem

Must first learn the user’s preferences from the ratings that the user gives

New Item Problem

Until the new item is rated by a substantial number of users, the recommender system would not be able to recommend it

User’s rating problem

Different users might use different scales

Sparsity

The number of ratings already obtained is usually very small compared to the number of ratings that need to be predicted

Scalability

Computing cost grows with C X S space

System typically have to search millions of users and items, it causes a serious scalability problem

However, these correlations will change when new users are added

Adaptability

Requirement of a user may change over time


Surveys on Hybrid method

Combining separate recommender Linear combination of two outputs Voting scheme

Adding Content-based to Collaborative model Add Content-based profile for each user Use filterbot, the virtual user

Adding Collaborative to Content-based model Add user profiles presented by term vector for each items

Single unifying model Knowledge-based techniques

– Entrée uses some domain knowledge– Quickstep, Foxtrot system uses topic ontology


Extending capabilities

Comprehensive understanding of Users and Items

Profiles in pure content-based and collaborative-based still tend to be quite simple and do not utilize some of the more advanced profiling techniques

In addition to using traditional profile features, such as keywords and simple user demographics more advanced profiling techniques based on data mining rules, sequences, and signatures that describe a user’s interests can be used to build user profiles


17

Extending capabilities (cont’d)

Multidimensionality of Recommendations

Current recommendation system uses only 2-dimension

– User x Item

We can extend dimension of recommendation

– Context(TPOK), Demographic information, …


18


Example of multidimension : The movie

Traditional recommendation consider just 2 space

– Who is the user?

– What movie?

We can consider other information

– Characteristics of the movie?

– Person wants to see movie?

– Where and how the movie will be seen?

– With whom the movie will be seen?

– When will the movie be seen?



Multicriteria Rating

To expand rating criteria

Taking a linear combination of multiple criteria and reduc-ing the problem to a single-criterion optimization problem

Optimizing the most important criterion and converting other criteria to constraint



Restaurant example :



Nonintrusiveness

The problem of feedback normalizing

One way to explore the intrusiveness problem is to deter-mine an optimal number of ratings the system should ask from a new user

This topic is related to Opinion Mining



Flexibility

Most of the recommendation methods are “hard-wired” into the systems

Therefore, the end-user cannot customize recommendations ac-cording to his or her needs in real time.

Also, most of the recommender systems recommend only individ-ual items to individual users and do not deal with aggregation.

However, it is important to be able to provide aggregated recom-mendations in a number of applications, such as recommend brands or categories of products to certain segments of users (e.g. Vacations in Florida - Students).

One way to support aggregated recommendations is by utilizing the OLAP-based approach.

Recommendation Query Language (RQL)


23


RQL is SQL-like language for expressing flexible user-speci-fied recommendation requests

“recommend to each user from New York the best three movies that are longer than two hours” can be ex-pressed in RQL”.

toward the next generation of recommender systems 2008. 11.05 ieee transactions on knowledge and...

Documents

user c recommendation

rating based recommendation

particular user

recommendation engine

keymatching user preferences

various user characteristics

gihyun gong copyright

textbased itemsthe content