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Interactive Itinerary Planning
"... Planning an itinerary when traveling to a city involves substantial effort in choosing PointsofInterest (POIs), deciding in which order to visit them, and accounting for the time it takes to visit each POI and transit between them. Several online services address different aspects of itinerary pla ..."
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Planning an itinerary when traveling to a city involves substantial effort in choosing PointsofInterest (POIs), deciding in which order to visit them, and accounting for the time it takes to visit each POI and transit between them. Several online services address different aspects of itinerary planning but none of them provides an interactive interface where users give feedbacks and iteratively construct their itineraries based on personal interests and time budget. In this paper, we formalize interactive itinerary planning as an iterative process where, at each step: (1) the user provides feedback on POIs selected by the system, (2) the system recommends the best itineraries based on all feedback so far, and (3) the system further selects a new set of POIs, with optimal utility, to solicit feedback for, at the next step. This iterative process stops when the user is satisfied with the recommended itinerary. We show that computing an itinerary is NPcomplete even for simple itinerary scoring functions, and that POI selection is NPcomplete. We develop heuristics and optimizations for a specfic case where the score of an itinerary is proportional to the number of desired POIs it contains. Our extensive experiments show that our algorithms are efficient and return high quality itineraries. Abstract — 1 I.
1 Composite Retrieval of Diverse and Complementary Bundles
"... 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 lowcarb nutrition, a set of places to visit on holidays). In this article, we argue tha ..."
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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 lowcarb nutrition, a set of places to visit on holidays). In this article, 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 characterize its complexity. We prove that the problem in its general form is NPhard 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 us how to adopt a twophase approach (ProduceandChoose, or PAC) in which we first produce many valid bundles, and then we choose k among them. For the first phase we devise two adhoc clustering algorithms, while for the second phase we adapt heuristics with approximation guarantees. We also devise another approach which is based on first finding a kclustering and then selecting a valid bundle from each of the produced clusters (ClusterandPick, or CAP). We compare experimentally the proposed methods on a large realworld database of usergenerated restaurant reviews from Yahoo! Local, exploring their performance under a variety of settings. 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.
ComprehensionBased Result Snippets
"... Result snippets are used by most search interfaces to preview query results. Snippets help users quickly decide the relevance of the results, thereby reducing the overall search time and effort. Most work on snippets have focused on text snippets for Web pages in Web search. However, little work has ..."
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Result snippets are used by most search interfaces to preview query results. Snippets help users quickly decide the relevance of the results, thereby reducing the overall search time and effort. Most work on snippets have focused on text snippets for Web pages in Web search. However, little work has studied the problem of snippets for structured data, e.g., product catalogs. Furthermore, all works have focused on the important goal of creating informative snippets, but have ignored the amount of user effort required to comprehend, i.e., read and digest, the displayed snippets. In particular, they implicitly assume that the comprehension effort or cost only depends on the length of the snippet, which we show is incorrect for structured data. We propose novel techniques to construct snippets of structured heterogeneous results, which not only select the most informative attributes for each result, but also minimize the expected user effort (time) to comprehend these snippets. We create a comprehension model to quantify the effort incurred by users in comprehending a list of result snippets. Our model is supported by an extensive userstudy. A key observation is that the user effort for comprehending an attribute across multiple snippets only depends on the number of unique positions (e.g., indentations) where this attribute is displayed and not on the number of occurrences. We analyze the complexity of the snippet construction problem and show that the problem is NPhard, even when we only consider the comprehension cost. We present efficient approximate algorithms, and experimentally demonstrate their effectiveness and efficiency.
Efficient Rank Join with Aggregation Constraints
"... We show aggregation constraints that naturally arise in several applications can enrich the semantics of rank join queries, by allowing users to impose their applicationspecific preferences in a declarative way. By analyzing the properties of aggregation constraints, we develop efficient determinis ..."
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We show aggregation constraints that naturally arise in several applications can enrich the semantics of rank join queries, by allowing users to impose their applicationspecific preferences in a declarative way. By analyzing the properties of aggregation constraints, we develop efficient deterministic and probabilistic algorithms which can push the aggregation constraints inside the rank join framework. Through extensive experiments on various datasets, we show that in many cases our proposed algorithms can significantly outperform the naive approach of applying the stateoftheart rank join algorithm followed by postfiltering to discard results violating the constraints. 1.
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, 2012
"... Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summ ..."
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Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient information retrieval etc. Generating article bundles from a large number of input articles, based on the associated features of the articles is tedious and computationally expensive task. In this report we propose an automatic twostep approach for topic extraction and bundling of related articles from a set of scientific articles in realtime. For topic extraction, we make use of Latent Dirichlet Allocation (LDA) topic modeling techniques and for bundling, we make use of hierarchical agglomerative clustering techniques. We run experiments to validate our bundling semantics and compare it with existing models in use. We make use of an online crowdsourcing marketplace provided by Amazon called Amazon Mechanical Turk to carry out experiments. We explain our experimental setup and empirical results in detail and
Shortlisting TopK Assignments
"... In this paper we identify a novel query type, the topK assignment query (αTopK). Consider a set of objects P and a set of suppliers S, where each object pi ∈ P must be assigned to one supplier sj ∈ S. Assume that there is a cost cij associated with every objectsupplier pair 〈pi, sj〉. The matching ..."
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In this paper we identify a novel query type, the topK assignment query (αTopK). Consider a set of objects P and a set of suppliers S, where each object pi ∈ P must be assigned to one supplier sj ∈ S. Assume that there is a cost cij associated with every objectsupplier pair 〈pi, sj〉. The matching with the smallest total cost would assign each object pi to the supplier sj with the minimum cij value. In many scenarios, however, runnerup assignments may be required too, like for example when a decision maker needs to make additional considerations, not captured by cij values. In this case, it is necessary to examine several shortlisted assignments before choosing one. This motivates the αTopK query, which computes the K best assignments, i.e., those achieving the K smallest total costs. Algorithms for the traditional assignment ranking problem could be adapted to process the query, but their time requirements are prohibitive for large datasets (cubic to the input size). In this work we exploit the specific properties of the αTopK problem and develop scalable methods for its processing. We also consider its incremental version, where K is not specified in advance; instead, the best assignments are iteratively computed on demand. An empirical evaluation with real data verifies the practicality and efficiency of our framework. 1.
Finding the Right Set of Users: Generalized Constraints for Group Recommendations ABSTRACT
"... Recently, group recommendations have attracted considerable attention. Rather than recommending items to individual users, group recommenders recommend items to groups of users. In this position paper, we introduce the problem of forming an appropriate group of users to recommend an item when constr ..."
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Recently, group recommendations have attracted considerable attention. Rather than recommending items to individual users, group recommenders recommend items to groups of users. In this position paper, we introduce the problem of forming an appropriate group of users to recommend an item when constraints apply to the members of the group. We present a formal model of the problem and an algorithm for its solution. Finally, we identify several directions for future work.
Evaluation of Setbased Queries with Aggregation Constraints
"... Many applications often require finding a set of items of interest with respect to some aggregation constraints. For example, a tourist might want to find a set of places of interest to visit in a city such that the total expected duration is no more than six hours and the total cost is minimized. W ..."
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Many applications often require finding a set of items of interest with respect to some aggregation constraints. For example, a tourist might want to find a set of places of interest to visit in a city such that the total expected duration is no more than six hours and the total cost is minimized. We refer to such queries as SAC queries for “setbased with aggregation constraints ” queries. The usefulness of SAC queries is evidenced by the many variations of SAC queries that have been studied which differ in the number and types of constraints supported. In this paper, we make two contributions to SAC query evaluation. We first establish the hardness of evaluating SAC queries with multiple count constraints and presented a novel, pseudopolynomial time algorithm for evaluating a nontrivial fragment of SAC queries with multiple sum constraints and at most one of either count, groupby, or content constraint. We also propose a heuristic approach for evaluating general SAC queries. The effectiveness of our proposed solutions is demonstrated by an experimental performance study.
Multiobjective Optimal Combination Queries
"... Abstract. Multiobjective optimization problem finds out optimal objects w.r.t. several objectives rather than a single objective. We propose a new problem called a multiobjective optimal combination problem (MOC problem) which finds out object combinations w.r.t. multiple objectives. A combination ..."
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Abstract. Multiobjective optimization problem finds out optimal objects w.r.t. several objectives rather than a single objective. We propose a new problem called a multiobjective optimal combination problem (MOC problem) which finds out object combinations w.r.t. multiple objectives. A combination dominates another combination if it is not worse than anther one in all attributes and better than another one in one attribute at least. The combinations, which cannot be dominated by any other combinations, are optimal. We propose an efficient algorithm to find out optimal combinations by reducing the search space with a lower bound reduction method and an upper bound reduction method based on the Rtree index. We implemented the proposed algorithm and conducted experiments on synthetic data sets.