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