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

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

2. 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?

3. 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.

4. 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

5. Novel rec. problems • Application 3: Buy a camera and accessories under constraints OR How to find a bunch of interesting podcasts / songs / movies to kill the next 10 boring hours on the plane? How to find a pack of tweeters to follow without being overwhelmed?

6. 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.

7. 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

8. Composite RS – An Architecture Item Recommender Item Rating t1 Item Recommendation t2 t3 Package Recommendation Cost Budget Composite Recommender Item Recommendation Compatibility Checker p1 External Cost Source p2

9. 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.

10. 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)

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

12. 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.

13. 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.

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

15. 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

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

17. Example • Cost Budget : 10 • α = 2 • cmin = 2 • Best possible unseen items

18. 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. RecSys2010-s- for more details.

19. 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 futurerecommendations. • 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 Flow of information • Because of these ratings, the item may be recommended to some other users. • Some users rate the item • (seed users) RecMax: Can we strategically select the seed users?

21. RecMax 21 Flow of information • Recommendees • Seed Users Select kseed users such that ifthey provide high ratings to a new product, thenthe number of other users to whom the product is recommended (hit score) by the underlying recommender system algorithm is maximum.