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.
872843
Active feedback in ad hoc information retrieval
Information retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retrieval system can actively probe a user with questions to clarify the information need instead of just passively responding to user queries. A basic question is thus how a retrieval system should propose questions to the user so that it can obtain maximum benefits from the feedback on these questions. In this paper, we study how a retrieval system can perform active feedback, i.e., how to choose documents for relevance feedback so that the system can learn most from the feedback information. We present a general framework for such an active feedback problem, and derive several practical algorithms as special cases. Empirical evaluation of these algorithms shows that the performance of traditional relevance feedback (presenting the top K documents) is consistently worse than that of presenting documents with more diversity. With a diversity-based selection algorithm, we obtain fewer relevant documents, however, these fewer documents have more learning benefits.
6914367
Context-sensitive information retrieval using implicit feedback
A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.
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.
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.
28331390
Optimal Routing of Permutations on Rings
36077313
Developing High-Performance Parallel Applications Using EPAS
In spite of the advent of high performance parallel computers and commodity clusters, complexity of parallel application development remains one of the major obstacles towards the mainstream adoption of parallel computing. Researchers are constantly investigating different approaches to reduce parallel application development time and increase productivity. As re-usable components, patterns have gained popularity in the sequential programming domain. Subsequently, several pattern-based parallel programming environments (PPEs) have been proposed to facilitate parallel application development procedure. Unfortunately, most of these PPEs lack the required flexibility in order to develop real-life parallel applications. In this paper, we describe the features of the EPAS (Extended Parallel Architectural Skeleton) PPE that enables development of complex parallel applications. We investigate and design the required patterns, and then use them to develop a parallel data cube computing application. Finally, we present the performance of the developed applications and discuss the results.
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.
25793995
Update-based cache replacement policies in wireless data access
Most of cache replacement schemes are access-based replacement policies since they are based on object access frequency/time information. However, update information is extremely important since an updated object makes itself invalid, and the object hit information becomes useless. In this paper, we propose two update-based replacement policies, the least access-to-update ratio (LA2U) and least access-to-update difference (LAUD) in wireless data access, based on both update frequency and access frequency. Extensive simulations have been carried out to evaluate the proposed policies. Simulation results show that the proposed update-based policies outperform access-based policies at most cases. It is concluded that considering update information in designing replacement policies can increase cache performance, especially, when updates are heavy
16080938
Locality phase prediction
As computer memory hierarchy becomes adaptive, its performance increasingly depends on forecasting the dynamic program locality. This paper presents a method that predicts the locality phases of a program by a combination of locality profiling and run-time prediction. By profiling a training input, it identifies locality phases by sifting through all accesses to all data elements using variable-distance sampling, wavelet filtering, and optimal phase partitioning. It then constructs a phase hierarchy through grammar compression. Finally, it inserts phase markers into the program using binary rewriting. When the instrumented program runs, it uses the first few executions of a phase to predict all its later executions.Compared with existing methods based on program code and execution intervals, locality phase prediction is unique because it uses locality profiles, and it marks phase boundaries in program code. The second half of the paper presents a comprehensive evaluation. It measures the accuracy and the coverage of the new technique and compares it with best known run-time methods. It measures its benefit in adaptive cache resizing and memory remapping. Finally, it compares the automatic analysis with manual phase marking. The results show that locality phase prediction is well suited for identifying large, recurring phases in complex programs.