In this journal, we explore the impact of space constraints on maintaining per-user and pairwise item lists and develop two complementary solutions that leverage shared user behavior to maintain the efficiency of our group recommendation algorithms within a space budget. The ?rst solution, behavior factoring, factors out user agreements from disagreement lists, while the second solution, partial materialization, selectively materializes a subset of disagreement lists.

Facetedpedia is a faceted search system that dynamically discovers query-dependent faceted interfaces for Wikipedia search result articles.

We investigate how to assist user in online shopping by suggesting packages to her with the item she is primarily interested to purchase (central item) (e.g., the accessories of iPhone as a package during iPhone purchase). In particular, we propose to build composite items which associates a central item with a set of packages, formed by satellite items, and help users explore them. We define and study the problem of effective construction and exploration of large sets of packages associated with the central item, and design and implement efficient algorithms for solving the problem in two stages: summarization, and visual effect optimization.

Wikipedia has become the largest encyclopedia ever created, with close to 3 million English articles by far. We propose FacetedPedia, a faceted retrieval system for information discovery and exploration over Wikipedia. Given the set of articles resulting from a keyword search query, FacetedPedia dynamically and automatically discovers a faceted interface for navigating and exploring the result articles.

The need for group recommendation arises in many scenarios: a movie for friends to watch together, a travel destination for a family to spend a holiday break, and so on. We consider the problem of group recommendation where each group is formed by a set of users. The central idea is to aim at returning items which are more likely to be liked by each member in the group. We investigate several properties critical to group recommendation, and design efficient algortihms.

In this demo, we present DynaCet - a domain independent system that provides effective minimum-effort based dynamic faceted search solutions over enterprise databases. At every step, Dynacet suggests facets depending on the user response at previous step. Facets are selected based on their ability to rapidly drill down to the most promising tuples, as well as on the ability of the user to provide desired values for them. The benefits provided include faster access to information stored in databases while taking into consideration the variance in user knowledge and preferences.

We investigate opportunities to improve the performance of minimum effort driven Faceted search techniques. The main idea is motivated by the early stopping techniques used in the TA-family of algorithms for top-k computations.

We investigate how Faceted Search can be enabled over structured databases for tuple search. Our Facet Selection techniques rely on selecting facets dynamically based on user response using minimum effort based techniques in principle.

We develop Computational geometry based algorithms and approximation algorithms for Intrusion detection in Wireless Sensor Networks in the presence of obstacles. Details can be found at http://dbxlab.uta.edu/sensor.htm.

The aim of this work is to develop an edge preserving image compression technique using one hidden layer feed forward neural network of which the neurons are determined adaptively. The network is trained using the single processed image block. The work proposes initialization of weights between the input and lone hidden layer by transforming pixel coordinates of the input pattern block into its equivalent one-dimensional representation.