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

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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?

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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.

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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

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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?

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Package/Set Recommendation

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Breaking out of the box

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

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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.

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So what’s the problem, again?

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Composite Recommendation Problem Background cost information – Assumed in this paper. Global minimum item cost. – More sophisticated alternative possible E.g., Histogram

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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.

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Compatibility

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Efficient Package Recommendation System Overview Composite Recommendation – Instance Optimal Approximation Algorithm – Heuristic based Approximation Algorithm – Handling Compatibility Empirical Study Related Work

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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

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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

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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

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Instance Optimality of InsOpt-CR

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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.

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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?

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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.

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