L18-21_finalnew [Compatibility Mode].pdf

8/9/2019 L18-21_finalnew [Compatibility Mode].pdf

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BITS PilaniPilani Campus

BITS Pilani

presentationN.MEHALA

FACULTY,CS/IS GROUP


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BITS Pilani, Deemed to be University under Section 3 of UGC Act, 1956

INFORMATION RETRIEVAL

4/14/2015 2CS F469

CS F469Second Semester 2014-15


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BITS Pilani, Pilani Campus

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What is Recommendation systems?

Items

Search Recommendations

Products, web sites, blogs, news items, …

Recommendation systems areprograms which attempt to predict

items that a user may be interested inThese systems serve two important

functions:They help users deal with theinformation overload by giving

them recommendations ofproducts, etc.They help businesses makemore profits, i.e., selling moreproducts.


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Example applications


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BITS Pilani, Pilani Campus5

What book should I buy?


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What news should I read?


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The Recommendation Problem• We have a set of users U and a set of items S to

be recommended to the users.

• Let p be an utility function that measures the

usefulness of item s (∈∈∈∈ S ) to user u (∈∈∈∈ U ), i.e.,

– p :U ×

S→

R , where R is a totally ordered set(e.g., non-negative integers or real numbers ina range)

• Objective – Learn p based on the past data

– Use p to predict the utility value of each item s

(∈ S ) to each user u (∈ U ) 7


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As Prediction

• Rating prediction, i.e., predict the rating score

that a user is likely to give to an item that s/hehas not seen or used before. E.g.,

– rating on an unseen movie. In this case, the

utility of item s to user u is the rating given to s by u .

• Item prediction, i.e., predict a ranked list of items

that a user is likely to buy or use.

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Two basic approaches

• Content-based recommendations:

– The user will be recommended items similar tothe ones the user preferred in the past

• Collaborative filtering (or collaborative

recommendations):

– The user will be recommended items thatpeople with similar tastes and preferences liked

in the past.

• Hybrids: Combine collaborative and content-based methods.

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Content-BasedRecommender System


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Plan of action

likeslikes

Item profilesItem profiles

RedRedCirclesCirclesTrianglesTriangles

User profileUser profile

matchmatch

recommendrecommend buildbuild


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Content-Based Recommendation• In content-based recommendations the system

tries to recommend items that matches the UserProfile.

• The Profile is based on items user has liked in the

past or explicit interests that s/he defines.• A content-based recommender system matches

the profile of the item to the user profile to decide

on its relevancy to the user.


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Simple Example

Read update User Profile

New books User Profile

RecommenderSystems

Match

recommendation


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Advantages

• No need for data on other users

– No cold-start or sparsity problem

• Able to recommend to users with unique tastes

• Able to recommend new and unpopular item

– No first-rater problem

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Limitations of content-based approach

• Finding the appropriate features

– e.g., images, movies, music• Overspecialization

– Never recommends items outside user’s

content profile – People might have multiple interests

• Recommendations for new users

– How to build a profile? – A new user, having very few ratings, would

not be able to get accurate recommendations.


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Collaborative Filtering

• Consider user c

• Find set D of other users whose ratings are“similar” to c’s ratings

• Estimate user’s ratings based on ratings of

users in D

Set of other usersSimilar

RatingsRatings

Estimate


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Limitation of Collaborative method• User’s rating problem

– Different users might use different scales• Sparsity (Long Tail problem)

– The number of ratings already obtained is usually very smallcompared to the number of ratings that need to be predicted

• Scalability – 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

• 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, therecommender system would not be able to recommend it


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Hybrid Methods

• Content-based and collaborative methods have

complementary strengths and weaknesses• Combine methods to obtain the best of both

• Various hybrid approaches:

– Apply both methods and combine recommendations

– Use collaborative data as content

– Use content-based predictor as another collaborator

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Search engines andrecommender systems


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Search Engines vs.

Recommender Systems

Search Engines

Goal – answer users adhoc queries

Input – user ad-hoc needdefined as a query

Output- ranked itemsrelevant to user need(based on user

preferences???)Methods - Mainly IRbased methods

Recommender Systems

Goal – recommend servicesor items to user

Input - user preferencesdefined as a profile

Output - ranked items basedon user preferences

Methods – variety ofmethods, IR, ML


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Query Recommender

• Content based or content ignorant or hybrid

recommender for queries using informationabout documents

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Content-Boosted Collaborative Filtering

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BITS Pilani, Pilani Campus14.04.2015

Context-Based Recommender Systems

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The recommender system uses additional data about

the context of an item consumption.For example, in the case of a restaurant the time or

the location may be used to improve therecommendation compared to what could be

performed without this additional source ofinformation.

A restaurant recommendation for family celebration

should be different than a restaurant recommendationfor official party.


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BITS Pilani, Pilani Campus14.04.2015

Recommend a vacation

Winter vs. summer

Recommend a purchase (e-retailer)

Gift vs. for yourself

Context-Based Recommender Systems:Motivating Examples

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What is the user when asking for a

recommendation?Where (and when) the user is ?

What does the user (e.g., improve hisknowledge or really buy a product)?

Is the user or with other ?

Are there products to choose or only ?

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What simple recommendation techniques ignore?


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Major obstacle for contextual computing

Obtain sufficient and reliable data describing the user context

Selecting the right information, i.e., relevant in a particular

personalization task

Understand the impact of contextual dimensions on the

personalization process

Computational model the contextual dimension in a more

classical recommendation technology

For instance: how to extend Collaborative Filtering to include

contextual dimensions?

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Evaluating Recommendations• Precision

– Accuracy of predictions

– Compare predictions with known ratings

• Recommendation Quality

– Top-n measures (e.g., Breese score)

• Item-Set Coverage – Number of items/users for which system can make

predictions


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Evaluating Recommendations

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Receiver operating characteristic (ROC)• Tradeoff curve between false positives and falsenegatives

Assuming the confusion matrix above,its corresponding table of confusion,for the cat class, would be:


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The false positive rate is FPR= .Where FP is number of false positives, and TN is number of true negatives.

True positive rate (or sensitivity): TPR =TP /(TP +FN )

It is a plot of the true positive rate against the false positive rate forthe different possible cutpoints of a test.

Receiver operating characteristic (ROC)

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