Is there anything more to RS than just recommending movies and songs?

Problem 1: Recommending Composite Objects

• Sets of items (e.g., camera and accessories) • Sequences (list of songs) • Weighted paths (a tour of POIs) • More complex structures?

Novel recommendation problems

• Application 1: Travel Planning!• User visits Vancouver for the first time. • Has one day to spare. • Wants to keep the budget, say, under $500. • Maybe additional constraints on time,

preferred routes etc.

Novel Rec. problems

• Application 2: Bundle Shopping!• User wants to buy a smart phone &

accessories • Looking for smart phone plus contract• Budget aware, requirements on minutes &

data

Novel rec. problems

• Application 3: Buy a camera and accessories under constraints OR

How to find a pack of tweeters to follow

without being overwhelmed?

How to find a bunch of

interesting podcasts / songs / movies to kill the next 10 boring

hours on the plane?

Package/Set Recommendation • We’ll discuss a simple extension of the standard paradigm of

recommendations: – Recommend top- sets (aka packages) of items instead of individual items. – There are natural constraints at play. – Efficiency is key. – Want a non-obtrusive extension to existing RS, which could use any

method.

Based on Min Xie et al. Breaking out of the box of recommender systems. RecSys 2010. Caveat: Midway, we will take flight out of this paper to the topic of top- query processing in databases, a needed b/g.

Breaking out of the box

• Item Recommendation Package

Recommendation

– Leverage on existing item recommender system

– Automatic top-k package recommendation

• User specified cost budget

• Compatibility cost

Composite RS – An Architecture Item Recommender

Item Rating

Item Recommendation

Cost BudgetPackage

Recommendation

Item Recommendation Composite Recommender

External Cost Source

CompatibilityChecker

t1

t2

t3

p1

p2

What’s the Composite RS Problem? • Input to the composite recommender system

– Item rating / value obtained from item recommender system• Items are accessed in the non-increasing order of their ratings

– Item cost information obtained from external cost source• Can either be obtained for “free” or randomly accessed from cost

source

• Access Cost– Sorted Access Cost + Random Access Cost # of items accessed.

So what’s the problem, again? • Top-Composite Recommendation

– Itemset ordered by aggregate rating– External cost information source– Cost Budget– An integer – Find top- packages P1 , …, Pk which have the highest total value

and are under the cost budget. – The items in the package may be of different types:

• E.g., parks, museums, restaurants, and shows. • Can glue together diff. RS recommending different types of items.

• When k = 1 , classical knapsack problem :– Access Constraint (through getNext() API)

Composite Recommendation Problem

• Background cost information– Assumed in this paper. • Global minimum item cost.

– More sophisticated alternative possible• E.g., Histogram

Criteria for the CompRec Problem

• Generate high quality package recommendations automatically– Quality ::= Sum of (predicted) item ratings in the

package

• Minimize number of items to be accessed, i.e., #getNextBest(.) calls to RS.

Compatibility• Boolean Compatibility Examples

– For trip planning, the user may require the result package to contain no more than 3 museums, 1 park.

– For tweeter recommendation, the user may require no more than one tweeter on general news (e.g., either CNN or NYTimes)

• More Complex Compatibility Example– For trip planning, the user may require the time spent on the

travel to be less than 5 hours, i.e., given a specific transportation method, the minimum length tour of all POIs contained in the package should be less than a budget B.

• How to do this efficiently? – Will resume this after top- query processing.

Efficient Package Recommendation

• System Overview• Composite Recommendation– Instance Optimal Approximation Algorithm– Heuristic based Approximation Algorithm– Handling Compatibility

• Empirical Study• Related Work

Quality Guarantee & Access Cost Minimization

• Approximation Algorithm (V.V. Vazirani’01)– α approximation (1 < α)

• Recall: Instance Optimality (Fagin et.al. PODS’01)– Given a class of algorithms, a class of input instances– Given a cost function (# of items accessed)– Guarantee the cost of the proposed algorithm on

any instance is at most β times the cost of any algorithm in the same class

Instance Optimal Approximation Algorithm

• Algorithm Template (Top-1)

• Quality guarantee– α-approximation (For simplicity of presentation, and optimization

opportunity, we consider 2-approximation in this work)

Access items from RecSys

Calculate Upper Bound Value of Optimal Solution Check stop criteria

Calculate optimal solution using seen items

N: Input items, B: BudgetBG: Background information

Cost Budget : 10 α = 2 cmin = 2

Best possible unseen items

Example

Item Rating Cost

t1 5 2t2 5 2t3 4 3t4 4 4t5 4 2t6 3 3t7 2 2t8 2 2t9 2 2

Stop

Instance Optimality of InsOpt-CR

• InsOpt-CR is instance optimal over the class of all possible α-approximation algorithms that are constrained to access items in non-increasing order of their value– InsOpt-CR has an instance optimality ratio of 1

• Read: Min Xie et al. Breaking out of the box of recommender systems. RecSys 2010 -s- for more details.

Problem 2: Combining the power of RS and SN

• When users rate items, those signals are used as a basis of future recommendations, i.e., user ratings influence future recommendations.

• Can we launch a targeted marketing campaign over an existing operational Recommender System?• Pick seed users for rating an item to produce a

large scale rec. of an item, by the RS? RecMax.

Amit Goyal and L. RecMax: Exploting Recommender Systems for Fun and Profit. KDD 2012.

20

Consider an item in a Recommender System

Some users rate the item(seed users)

Because of these ratings, the item may be

recommended to some other users.

Flow of information

RecMax: Can we strategically select the seed users?

RecMax21

Seed Users

Flow of information

Recommendees

Select k seed users such that if they provide high ratings to a new product,

then the number of other users to whom the product is recommended (hit score) by the underlying

recommender system algorithm is maximum.