Internet users constantly make searches to find objects or results of their interest, generally through terms or phrases. Traditional search offers only solutions that take into account just the individual characteristics of the results, and not the relations they have with the rest of the universe. Typically, we are given an ordered list of the results related to the search criterion, which implies the need of changing several times the terms of the query to get to a solution that is more adequate to the intended search goal. As a solution to this problem, Composite Retrieval proposes that the results to a query may be grouped in sets of items (bundles), related through some similarity criterion, but at the same time are complementary. In this work we propose heuristic algorithms for Composite Retrieval which are evaluated experimentally, showing performance improvements over the previous results presented in the literature.

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${\bf NP}$-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.

Web search engines are systems devised to cope with highly dynamic and demanding query rates. These systems are composed by several services, each one dedicated to execute a single operation required to solve a query. Services are allocated into thousand of multi-core processors organized in racks. Some services communicate more than others, so it is desirable to put them in the same rack. In this work, we present and evaluate deployment algorithms for a web search engine. In particular, we focus on vertical search engines with specific on-line content which may include shopping, advertisement, etc.. Our goal is to improve the performance of a search engine by reducing communication time. Communication among services is performed by means of a fast and complex network supporting parallel sending and receiving of messages. We propose a communication-graph-based method, in which processors are the nodes of the graph and communication between them is represented as weighted arcs. Results show that our proposal method is able to reduce maximum query response time.

In this paper we deal with the efficiency oftoken-based strategies for the basic problem of controlling the allocation of a shared resource in a ring of n processing entities. We propose new protocols that allow a bounded number of exchanged messages per access request to the resource, while this amount is unbounded for classical solutions. We also guarantee all the requests to be served within a maximum delay. The new proposed protocols are request-message-based strategies, in that a process entity sends a message to “inform” the token of the access request.