Trusting the Trusted: An Empirical Analysis

One aspect of research in the area of bootstrapping of cold start users in trust-aware recommender systems is on guiding new users find trusted users in the trust network. In this paper we examine the question “Which type of users do cold start users actually trust?” We analyze the users trusted by cold start users in the Epinions dataset (Massa & Avesani, 2006a). We examine the set of trusted users on the basis of four critical parameters: number of outgoing links, number of incoming links, number of items rated and a hybrid parameter defined as preference score. We also experimentally evaluate the impact of the four parameters on prediction accuracy. Our analysis shows that cold start users will more likely trust a user with higher number of incoming links as a trusted user even though trusting users with high preference score would result in better prediction accuracy

On the Asymptotic Performance of IDA*

Since best-first search algorithms such as A* require large amounts of memory, they sometimes cannot run to completion, even on problem instances of moderate size. This problem has led to the development of limited-memory search algorithms, of which the best known is IDA*. This paper presents the following results about IDA and related algorithms: The analysis of asymptotic optimality for IDA* in [10] is incorrect. There are trees satisfying the asymptotic optimality conditions given in [10] for which IDA* is not asymptotically optimal. To correct the above problem, we state and prove necessary and sufficient conditions for asymptotic optimality of IDA* on trees. On trees not satisfying our conditions, we show that no best-first limited-memory search algorithm can be asymptotically optimal. On graphs, IDA* can perform quite poorly. In particular, there are graphs on which IDA* does node expansions where N is the number of nodes expanded by A'. (Also cross-referenced as UMIACS-TR-95-22

Improving the Efficiency of Limited-Memory Heuristic Search

This paper describes a new admissible tree search algorithm called Iterative Threshold Search (ITS). ITS can be viewed as a much-simplified version of MA*[2], and a generalized version of MREC [15]. ITS's node selection and retraction (pruning) overhead is much less expensive than MA*'s. We also present the following results: 1. Every node generated by ITS is also generated by IDA*, even if ITS is given no more memory than IDA*. In addition, there are trees on which ITS generates O(N) nodes in comparison to O(N log N) nodes generated by IDA*, where N is the number of nodes eligible for generation by A*.2. Experimental tests show that if the heuristic branching factor is low and the node- generation time is high (as in most practical problems), then ITS can provide significant savings in both number of node generations and running time.3. Our experimental results also suggest that on the Traveling Salesman Problem, both IDA* and ITS are asymptotically optimal on the average if the costs between the cities are drawn from a fixed range. However, if the range of costs grows in proportion to the problem size, then IDA* is not asymptotically optimal. ITS's asymptotic complexity in the later case depends on the amount of memory available to it

Improving the Efficiency of Limited-Memory Heuristic Search

This paper describes a new admissible tree search algorithm called Iterative Threshold Search (ITS). ITS can be viewed as a much-simplified version of MA*, and a generalized version of MREC ITS's node selection and retraction (pruning) overhead is much less expensive than MA*'s. We also present the following results: 1. Every node generated by ITS is also generated by IDA*, even if ITS is given no more memory than IDA*. In addition, there are trees on which ITS generates 0(N) nodes in comparison to 0(N log N) nodes generated by IDA*, where N is the number of nodes eligible for generation by A*. 2. Experimental tests show that if the heuristic branching factor is low and the nodegeneration time is high (as in most practical problems), then ITS can provide significant savings in both number of node generations and running time. 3. Our experimental results also suggest that on the Traveling Salesman Problem, both IDA* and ITS are asymptotically optimal on the average if the costs between the cities are drawn from a fixed range. However, if the rake of costs grows in proportion to the problem size, then IDA* is not asymptotically optimal. ITS's asymptotic complexity in the latter case depends on the amount of memory available to it. (Also cross-referenced as UMIACS-TR-95-23

Manufacturing Cell Formation by State-Space Search

This paper addresses the problem of grouping machines in order to design cellular manufacturing cells, with an objective to minimize intercell flow. This problem is replaced to one of the major aims of group technology (GT): to decompose the manufacturing system into manufacturing cells that are as independent as possible.This problem is NP-hard. Thus, nonheuristic methods cannot address problems of typical industrial dimensions because they would require exorbitant amounts of computing time, while fast heuristic methods may suffer from sub-optimality.We present a branch-and-bound state-space search algorithm that attempts to overcome both these deficiencies. One of the major strengths of this algorithm is its efficient branching and search strategy. In addition, the algorithm employs the efficient Inter-Cell Traffic Minimization Method to provide good upper bounds, and computes lower bounds based on a relaxation of merging

ITS: An Efficient Limited-Memory Heuristic Tree Search Algorithm

This paper describes a new admissible tree search algorithm called Iterative Threshold Search (ITS). ITS can be viewed as a much-simplified version of MA* [1], and a generalized version of MREC [12]. We also present the following results: 1. Every node generated by ITS is also generated by IDA*, even if ITS is given no more memory than IDA*. In addition, there are trees on which ITS generates O(N) nodes in comparison to O(N log N) nodes generated by IDA*, where N is the number of nodes eligible for generation by A*. 2. Experimental tests show that if the node-generation time is high (as in most practical problems), ITS can provide significant savings in both number of node generations and running time. Our experimental results also suggest that in the average case both IDA* and ITS are asymptotically optimal on the traveling salesman problem. Introduction Although A* is usually very efficient in terms of number of node expansions [2], it requires an exponential amount of memory, and..

PRA: Massively Parallel Heuristic Search

In this paper we describe a variant of A* search designed to run on the massively parallel, SIMD Connection Machine. The algorithm is designed to run in a limited memory by use of a retraction technique which allows nodes with poor heuristic values to be removed from the open list, until such time as they may need reexpansion, more promising paths having failed. Our algorithm, called PRA* (for Parallel Retraction A*), is designed to maximize use of the Connection Machine's memory and processors. In addition, the algorithm is guaranteed to return an optimal path when an admissable heuristic is used. Results comparing PRA* to Korf's IDA* for the fifteen-puzzle show significantly fewer node expansions for PRA*. In addition, empirical results show significant parallel speedups, indicative of the algorithm's design for high processor utilization