DocumentCode :
105393
Title :
Composite Retrieval of Diverse and Complementary Bundles
Author :
Amer-Yahia, Sihem ; Bonchi, Francesco ; Castillo, Claris ; Feuerstein, Esteban ; Mendez-Diaz, Isabel ; Zabala, Paula
Author_Institution :
LIG, Grenoble, France
Volume :
26
Issue :
11
fYear :
2014
fDate :
Nov. 2014
Firstpage :
2662
Lastpage :
2675
Abstract :
Users are often faced with the problem of finding complementary items that together achieve a single common goal (e.g., a starter kit for a novice astronomer, a collection of question/answers related to low-carb nutrition, a set of places to visit on holidays). In this paper, we argue that for some application scenarios returning item bundles is more appropriate than ranked lists. Thus we define composite retrieval as the problem of finding k bundles of complementary items. Beyond complementarity of items, the bundles must be valid w.r.t. a given budget, and the answer set of k bundles must exhibit diversity. We formally define the problem and show that in its general form is NP-hard and that also the special cases in which each bundle is formed by only one item, or only one bundle is sought, are hard. Our characterization however suggests how to adopt a two-phase approach (Produce-and-Choose, or PAC) in which we first produce many valid bundles, and then we choose k among them. For the first phase we devise two ad-hoc clustering algorithms, while for the second phase we adapt heuristics with approximation guarantees for a related problem. We also devise another approach which is based on first finding a k-clustering and then selecting a valid bundle from each of the produced clusters (Cluster-and-Pick, or CAP). We compare experimentally the proposed methods on two real-world data sets: the first data set is given by a sample of touristic attractions in 10 large European cities, while the second is a large database of user-generated restaurant reviews from Yahoo! Local. Our experiments show that when diversity is highly important, CAP is the best option, while when diversity is less important, a PAC approach constructing bundles around randomly chosen pivots, is better.
Keywords :
computational complexity; information retrieval; optimisation; pattern clustering; NP-hard problem; PAC approach; ad-hoc clustering algorithm; approximation guarantee; complementary items; composite retrieval; diverse bundle; item bundles; k-clustering; two-phase approach; user generated restaurant reviews; Approximation algorithms; Approximation methods; Cities and towns; Clustering algorithms; Complexity theory; Linear programming; Polynomials; Composite retrieval; complementarity; diversity; maximum edge subgraph;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2014.2306678
Filename :
6742606
Link To Document :
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