10284885
ACES: a contextual engine for search
In web information retrieval, users often have to modify their queries several times before they can reach a page that meets their information need. During these interactions with the search engine, the user provides a lot of useful information to the search engine, which can be exploited to infer the user information need. For instance, if a user types “lemur” as the query, most of the pages returned by the search engine are about the animal lemur or lemur information retrieval toolkit. On the other hand, if the search engine incorporates the previous query submitted by the same user, say, “information retrieval”, the search engine can disambiguate the meaning of “lemur” and present pages that are related to lemur information retrieval toolkit. We design and implement a sessionbased search engine ACES(A Contextual Engine for Search), which puts the search in context[2]. Although there have been several attempts at building a personalized or contextual search engine [3] or session based search engines [1], our search engine has the following new features, which will be demonstrated:
7524839
A session-based search engine
In this poster, we describe a novel session-based search engine, which puts the search in context. The search engine has a number of session-based features including expansion of the current query with user query history and clickthrough data (title and summary of clicked web pages) in the same search session and the session boundary recognition through temporal closeness and probabilistic similarity between query terms. In addition, the search engine visualizes the rank change of web pages as different queries are submitted in the same search session to help the user reformulate the query.
14429022
Mining long-term search history to improve search accuracy
Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.
410513
Active Feedback - UIUC TREC-2003 HARD Experiments
In this paper, we report our experiments on the HARD (High Accuracy Retrieval from Documents) Track in TREC 2003. We focus on active feedback, i.e., how to intelligently propose questions for relevance feedback in order to maximize accuracy improvement in the second run. We proposed and empirically evaluated three different methods, i.e., top-k, gapped top-k, and k-cluster centroid, to extract a fixed number of text units (e.g. passage or document) for feedback. The results show that presenting the top k documents for user feedback is often not as beneficial for learning as presenting more diversified documents.
6579321
Discovering interesting patterns through user's interactive feedback
In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (ie, frequent patterns) has already been mined. Our goal is to help a particular user effectively discover interesting patterns according to his specific interest. Without requiring a user to explicitly construct a prior knowledge to measure the interestingness of patterns, we learn the user's prior knowledge from his interactive feedback. We propose two models to represent a user's prior: the log linear model and biased belief model. The former is designed for item-set patterns, whereas the latter is also applicable to sequential and structural patterns. To learn these models, we present a two-stage approach, progressive shrinking and clustering, to select sample patterns for feedback. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.
6496359
Implicit user modeling for personalized search
Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.
16973693
An adaptive handoff priority scheme for wireless MC-CDMA cellular networks supporting realtime multimedia applications
An adaptive handoff priority scheme is proposed for a packet switching multicode (MC)-CDMA cellular network supporting realtime multimedia applications. The scheme jointly considers the physical, link and network layer characteristics, and gives service priority to handoff calls by exploiting the transmission rate adaptability of multimedia calls to the available radio resources. Based on the scheme, a call admission region is derived with the estimated cell capacity and the proposed packet scheduling and partial packet integration scheme and used for call admission control and handoff management with the satisfaction of quality of service requirements for all multimedia traffic, where the QoS parameters include the wireless transmission bit error rate, packet loss rate and delay requirement. Simulation results are given to demonstrate the viability of the proposed scheme.
8438496
Permutation Routing in All-Optical Product Networks
In this paper we study permutation routing techniques for all-optical networks. Firstly, we show some lower bounds on the number of wavelengths needed for implementing any permutation on an all-optical network in terms of bisection of the network. Secondly, we study permutation routing on product networks by giving a lower bound on the number of wavelengths needed, and presenting permutation routing algorithms for the wavelength non-conversion and conversion models, respectively. Finally, we investigate permutation routing on a cube-connected-cycles network by showing that the number of wavelengths needed for implementing any permutation in one round is [2 log n], which improves on a previously known general result for bounded degree graphs by a factor of O(log 3 n) for this special case.
415324
Mobility information for resource management in wireless ATM networks
User mobility poses a significant technical challenge to network resource management in wireless ATM (Asynchronous Transfer Mode) networks. In order to guarantee quality of service (QoS) to mobile users and to achieve a high efficiency in network resource management, the information of mobile users' handoff at a future moment is essential for statistical multiplexing. This paper develops a novel fuzzy logic inference system to estimate the user mobility information for a wireless ATM network which uses a direct sequence code division multiple access (DS/CDMA) protocol. The estimation is based on measured pilot signal strengths from a number of the nearest base stations by the mobile user. Numerical results are presented to demonstrate the performance of the proposed technique under various path losses and channel shadowing conditions. The proposed technique can achieve simplicity, accuracy and low cost.
30859719
Rearrangeable Graphs
31888135
An automatic seal imprint verification approach
The automatic verification of seal imprints is a difficult pattern recognition problem and has a lot of applications in the oriental countries. Several approaches have been proposed in the past 10 years, but all of them were hard to implement in practice because of their drawbacks in the adaptability, reliability and practicality. In this paper, a new automatic seal imprint verification approach is presented. Due to the efficient and accurate registration method, a set of effective features and the elaborate implementations, this approach has satisfied most practical requirements and has been used for a practical check verification system in a Chinese bank.