Recommender Systems

Dr Carol HargreavesChief of Business Analytics

Institute of Systems Science

National University of Singapore

carol.hargreaves@nus.edu.sg

Agenda

• What are Recommender Systems?

• Tips for building a Recommender System

What are Recommender Systems?

Why Recommender Systems?Traditionally, the recommender problemhas been viewed as a prediction problemin which we have user profiles and targetitems, the recommender system’s task isto predict that user’s rating for that item,reflecting the degree of the user’spreference for that item.

There are many types of recommender systems,easily 500+

What are Collaborative Recommender Systems?

Collaborative filtering models – computing relationships betweenitems or alternatively between users; then predicts items (or ratingsfor new items) that the user may have an interest in.

Collaborative recommenders work best for a user who fits into aniche with many neighbours of similar taste

Great power of collaborative recommender systems is in its cross –genre or outside of the box recommendation ability.

• Example, listener’s who enjoy free jazz also enjoy classical music

A products neighbours are other products that tend to get similarratings when rated by the same user

What are Collaborative Recommender Systems?

Collaborative filter RecommenderSystems sometimes have sparsematrices – few users have rated thesame item

Imputation to make matrix dense• Can be very expensive as it

significantly increases the amountof data

• Inaccurate imputation may distantthe data considerably

What are Collaborative Recommender Systems?

Recent research suggest modellingdirectly the observed ratings onlywhile avoiding over-fitting usingsingular value decompositionmethods

Problem is overcome by ,modelbased approaches such as singularvalue decomposition, whichreduces the dimensionality of thespace in which comparisons takeplace.

What are Content-Based Recommender Systems?

Content Based Techniques – Challenges

Depend on well structured attributes that

align with preferences

Depend on having a reasonable distribution of attributes across items

Computing keyword vectors to describe

items

Building profiles of user preference

Predicting user interest in items

What are Content-Based Recommender Systems?

Have a start-up (cold) problem in that they must accumulate enough ratings tobuild a reliable classifier.

Typically, a system will deal with the cold start problem, wherein many userssimply have very few ratings, making it difficult to reach global conclusions ontheir tastes

A way to relieve this problem is to incorporate additional sources ofinformation about the users.

Content-Based Recommender systems can gather behavioural informationregardless of the user’s willingness to provide explicit ratings.

A retailer can use its customers purchases or browsing history to learn theirtendencies, in addition to the ratings those customers may supply. In Addition,demographics may also be used.

Tips for Building Recommender Systems

What other Factors need to be Considered?

Similarly, customers inclinations evolve, leading then toredefine their taste.

Thus, the system should account for temporal effects,reflecting the dynamic, time-drifting nature of user-iteminteractions.

Temporal Dynamics

In reality product perception and popularity constantlychange, as new selections emerge.

What other Factors need to be Considered?

Some systems work with crude binary representation, statingeither “probably likely to buy the product” or “probably notinterested in the product”.

It is valuable to attach confidence scores with the estimatedpreferences.

Confidence can stem from available values that describe thefrequency of actions, e.g., how much time the user watched acertain show

Varying Confidence Levels

Non-Personalized RecommendersRestaurant ratings {0,1,2,3} are averaged, i.e. {poor,

good, very good, excellent}

• Too many mediocre restaurants can get good scores.

• Too many good restaurants get mediocre scores

• Self selection bias. Only rate the restaurant if you have been to it within the last year.

• People who don’t like the restaurant don’t go, therefore, don’t rate

• Diversity of raters. It doesn’t take many people to rate them down for the average to tumble

Non-Personalized Recommenders

Normalise Data

Data is sparse

Users rate differently

Some rate ‘High’, other rate ‘Low’.

Some use more of the scale than others

Averaging ignores these differences

Normalization compensates for this

Types of Non-Personalized Recommenders

• Averages lack context

• You order a sundae, you want sauce…..

• Recommender system, looks for the most popular ketchup (sauce with the highest average)

• You get ‘Tomato Sauce’ (Ketchup) as it is the most popular sauce at that restaurant!

You need the context!!! - Product associations

• People who bought product A also bought product B & C.

• Need to know whether the products were bought together or in sequence.

• Uses my behaviour to make a recommendation

• Keeps logs with individual behaviours

• When people bought ice – cream, what else did they buy?

• Need to look at time profiles too!

Dr Carol Anne HargreavesInstitute of Systems Science

National University of Singaporecarol.hargreaves@nus.edu.sg