1111081
Adaptive User Profile Model and Collaborative Filtering for Personalized News
In recent years, personalized news recommendation has received increasing attention in IR community. The core problem of personalized recommendation is to model and track users’ interests and their changes. To address this problem, both content-based filtering (CBF) and collaborative filtering (CF) have been explored. User interests involve interests on fixed categories and dynamic events, yet in current CBF approaches, there is a lack of ability to model user’s interests at the event level. In this paper, we propose a novel approach to user profile modeling. In this model, user’s interests are modeled by a multi-layer tree with a dynamically changeable structure, the top layers of which are used to model user interests on fixed categories, and the bottom layers are for dynamic events. Thus, this model can track the user’s reading behaviors on both fixed categories and dynamic events, and consequently capture the interest changes. A modified CF algorithm based on the hierarchically structured profile model is also proposed. Experimental results indicate the advantages of our approach.
41575427
Ranking Web News Via Homepage Visual Layout and Cross-Site Voting
Reading news is one of the most popular activities when people surf the internet. As too many news sources provide independent news information and each has its own preference, detecting unbiased important news might be very useful for users to keep up to date with what are happening in the world. In this paper we present a novel method to identify important news in web environment which consists of diversified online news sites. We observe that a piece of important news generally occupies visually significant place in some homepage of a news site and import news event will be reported by many news sites. To explore these two properties, we model the relationship between homepages, news and latent events by a tripartite graph, and present an algorithm to identify important news in this model. Based on this algorithm, we implement a system TOPSTORY to dynamically generate homepages for users to browse important news reports. Our experimental study indicates the effectiveness of proposed approach.
38925274
Dynamic Workflow Modeling and Verification
The dynamic nature of incident command systems and their requirement for high flexibility raise a challenge to the research and implementation of workflows. The significance of applying formal approaches to the modeling and analysis of workflows has been well recognized and several such approaches have been proposed. However, these approaches require users to master considerable knowledge of the particular formalisms, which impacts their application on a larger scale. To address these challenges, we developed an intuitive, yet formal approach to workflow modeling, enactment and validation. In this paper, we further develop a set of theorems to support dynamic modeling, modification and on-the-fly verification of the workflows. A prototype has been implemented to demonstrate the feasibility of the theoretical approach.
43623999
A Partition-Based Parallel MPEG-2 Software Decoder
14581620
Network cache model for wireless proxy caching
We propose a wireless network cache model to facilitate the cooperative proxy caching in wireless base stations. A self-configured and adaptive virtual proxy graph (VPG) is used to achieve efficient data search, data cache and data replication. Based on demand, the aggregate effect of data caching, searching and replicating actions by individual proxies automatically distributes cached Web documents closer to the interested mobile clients. Our performance study demonstrates that the proposed individual-based wireless proxy caching scheme outperforms existing caching schemes in terms of different performance metrics.
766846
Application of neural networks to biological data mining: a case study in protein sequence classification
Biological data mining aims to extract signi cant information from DNA, RNA and proteins. The signi cant information may refer to motifs, functional sites, clustering and classi cation rules. This paper presents an example of biological data mining: the classi cation of protein sequences using neural netw orks. We proposenew tec hniques to extract features from protein data and use them in combination with the Ba yesianneural network to classify protein sequences obtained from the PIR protein database maintained at the National Biomedical Research Foundation. T o evaluate the performance of the proposed approach, we compare it with other protein classi ers built based on sequence alignment and machine learning methods. Experimental results sho w the high precision of the proposed classi er and the complementarity of the tools studied in the paper.
2286283
Query result ranking over e-commerce web databases
To deal with the problem of too many results returned from an E-commerce Web database in response to a user query, this paper proposes a novel approach to rank the query results. Based on the user query, we speculate how much the user cares about each attribute and assign a corresponding weight to it. Then, for each tuple in the query result, each attribute value is assigned a score according to its "desirableness" to the user. These attribute value scores are combined according to the attribute weights to get a final ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demonstrate that this ranking method can effectively capture a user's preferences.