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

Breaking out of the box

Composite RS – An Architecture Item Rating Item Recommendation Cost Budget Item Recommendation External Cost Source t1 t2 t3 p1p1 p2p2

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?

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

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 Access items from RecSys Calculate Upper Bound Value of Optimal Solution Check stop criteria Calculate optimal solution using seen items N: Input items, B: Budget BG: Background information

 Cost Budget : 10  α = 2  c min = 2  Best possible unseen items Example ItemRatingCost t1t1 52 t2t2 52 t3t3 43 t4t4 44 t5t5 42 t6t6 33 t7t7 22 t8t8 22 t9t9 22

Instance Optimality of InsOpt-CR

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.

Consider an item in a Recommender System 20 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?

RecMax 21 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.