2205670
Multiplicative Adaptive Algorithms for User Preference Retrieval
In contrast to the adoption of linear additive query updating techniques in existing popular algorithms for user preference retrieval, in this paper we design two types of algorithms, the multiplicative adaptive query expansion algorithm MA and the multiplicative adaptive gradient search algorithm MG, both of which use multiplicative query expansion strategies to adaptively improve the query vector. We prove that algorithm MA has a substantially better mistake bound than the Rocchio's and the Perceptron algorithms in learning a user preference relation determined by a linear classifier with a small number of non-zero coefficients over the real-valued vector space [0, 1]n.We also show that algorithm MG boosts the usefulness of an index term exponentially, while the gradient descent procedure does so linearly. Our work also generalize the algorithm Winnow in the following aspects: various updating functions may be used; multiplicative updating for a weight is dependent on the value of the corresponding index term, which is more realistic and applicable to real-valued vector space; and finally, a number of documents which may or may not be counterexamples to the algorithm's current classification are allowed. Practical implementations of algorithms MA and MG have been underway in the next stage development of our intelligent web search tools.
28060413
The Architecture of Yarrow: A Real-Time Intelligent Meta-Search Engine
140623
Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs
Although efficient identification of user access sessions from very large web logs is an unavoidable data preparation task for the success of higher level web log mining, little attention has been paid to algorithmic study of this problem. In this paper we consider two types of user access sessions, interval sessions and gap sessions. We design two efficient algorithms for finding respectively those two types of sessions with the help of some proposed structures. We present theoretical analysis of the algorithms and prove that both algorithms have optimal time complexity and certain error-tolerant properties as well. We conduct empirical performance analysis of the algorithms with web logs ranging from 100 megabytes to 500 megabytes. The empirical analysis shows that the algorithms just take several seconds more than the baseline time, i.e., the time needed for reading the web log once sequentially from disk to RAM, testing whether each user access record is valid or not, and writing each valid user access record back to disk. The empirical analysis also shows that our algorithms are substantially faster than the sorting based session finding algorithms. Finally, optimal algorithms for finding user access sessions from distributed web logs are also presented.
44651708
Personalized Web Search with Clusters
5722664
A Visualization System using Data Mining Techniques for Identifying Information Sources
The Visual Analysis System (VAS) was developed to couple emerging successes in data mining with information visualization techniques in order to create a richly interactive environment for information retrieval from the World Wide Web. VAS's retrieval strategy operates by first using a conventional search engine to form a core set,of retrieved documents. This core set is expanded by crawling from links in these documents to form a much larger set. The link topology of the larger set is then examined using data mining techniques to identify pages likely to be both relevant to the query and reliable as sources of information. Information visualization techniques are used to display the filtered set in a form amenable to the use of perceptual processes to filter and guide further user directed search. (Contains 16 references.) (MES) Reproductions supplied by EDRS are the best that can be made from the original document. N tr) 00 7171A Visualization System using Data Mining Techniques for Identifying Information Sources Richard H. Fowler, Tarkan Karadayi, Zhixiang Chen, Xiannong Meng, Wendy A. Lawrence Fowler Department of Computer Science University of Texas Pan American United States {fowler, tkaradayi, chen, meng, wfowler @panam.edu} PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
7399730
An Almost Linear Time 2.8334-Approximation Algorithm for the Disc Covering Problem
The disc covering problem asks to cover a set of points on the plane with a minimum number of fix-sized discs. We develop an O(n(logn)2(loglogn)2) deterministic time 2.8334-approximation algorithm for this problem. Previous approximation algorithms [7,3,6], when used to achieve the same approximation ratio for the disc covering problem, will have much higher time complexity than our algorithms.
15070183
A construction of interpolating wavelets on invariant sets
We introduce the concept of a refinable set relative to a family of contractive mappings on a metric space, and demonstrate how such sets are useful to recursively construct interpolants which have a multiscale structure. The notion of a refinable set parallels that of a refinable function, which is the basis of wavelet construction. The interpolation points we recursively generate from a refinable set by a set-theoretic multiresolution are analogous to multiresolution for functions used in wavelet construction. We then use this recursive structure for the points to construct multiscale interpolants. Several concrete examples of refinable sets which can be used for generating interpolatory wavelets are included.
4216648
Optimizing web search using web click-through data
The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.
11497116
Nowhere zero flows in line graphs
Abstract Cai an Corneil (Discrete Math. 102 (1992) 103–106), proved that if a graph has a cycle double cover, then its line graph also has a cycle double cover, and that the validity of the cycle double cover conjecture on line graphs would imply the truth of the conjecture in general. In this note we investigate the conditions under which a graph G has a nowhere zero k-flow would imply that L ( G ), the line graph of G, also has a nowhere zero k-flow. The validity of Tutte's flow conjectures on line graphs would also imply the truth of these conjectures in general.
207643
User Modeling for Efficient Use of Multimedia Files
It is very common that a user likes to collect many multimedia files of their interests from the web or other sources for his/her daily use, such as in emails, presentations, and technical documents. This paper presents algorithms to learn user models, in particular, user intention models and preference models from the usage of these files. Such usages include downloading, inserting, and sending multimedia files. A user intention model predicts when the user may want to involve some multimedia objects in his currently working environment (e.g., an email) and provides more convenient and accurate help to the user. A user preference model describes the types and classes of the user's favorite multimedia files and helps an offline crawler to autonomously collect more useful multimedia files for the user. The algorithms have been implemented in our media agents system and shown their effectiveness in user modeling.
15432149
Ubiquitous media agents for managing personal multimedia files
A novel idea of ubiquitous media agents is presented. Media agents are intelligent systems that are able to automatically collect and build personalized semantic indices of multimedia data on behalf of the user whenever and wherever he/she accesses/uses these multimedia data. The sources of these semantic descriptions are the textual context of the same documents that contain these multimedia data. The URLs of these multimedia data are indexed using these textual features. When the user wants to use these multimedia data once again, the media agents can also help the user find relevant multimedia data and provide proper suggestions based on the semantic indices. The media agents can also learn form the user's interaction records to refine the semantic indices and to model the user intentions and preferences. In our experiments, the media agents are effective in gathering relevant semantics for media objects and learning to provide precise suggestions when the user wants to re-use relevant media objects again.