Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

A Reactive Tabu Search Based Equalizer for Severely Delay-Spread UWB MIMO-ISI Channels

We propose a novel equalizer for ultrawideband (UWB) multiple-input multiple-output (MIMO) channels characterized by severe delay spreads. The proposed equalizer is based on reactive tabu search (RTS), which is a heuristic originally designed to obtain approximate solutions to combinatorial optimization problems. The proposed RTS equalizer is shown to perform increasingly better for increasing number of multipath components (MPC), and achieve near maximum likelihood (ML) performance for large number of MPCs at a much less complexity than that of the ML detector. The proposed RTS equalizer is shown to perform close to within 0.4 dB of single-input multiple-output AWGN performance at 10(-3) uncoded BER on a severely delay-spread UWB MIMO channel with 48 equal-energy MPCs.

Energy efficient asymmetric binary search switching technique for SAR ADC

An asymmetric binary search switching technique for a successive approximation register (SAR) ADC is presented, and trade-off between switching energy and conversion cycles is discussed. Without using any additional switches, the proposed technique consumes 46% less switching energy, for a small input swing (0.5 V-ref (P-P)), as compared to the last reported efficient switching technique in literature for an 8-bit SAR ADC. For a full input swing (2 V-ref (P-P)), the proposed technique consumes 16.5% less switching energy.

A Gibbs-ensemble based technique for Monte Carlo simulation of electric double layer capacitors (EDLC) at constant voltage

Current methods for molecular simulations of Electric Double Layer Capacitors (EDLC) have both the electrodes and the electrolyte region in a single simulation box. This necessitates simulation of the electrode-electrolyte region interface. Typical capacitors have macroscopic dimensions where the fraction of the molecules at the electrode-electrolyte region interface is very low. Hence, large systems sizes are needed to minimize the electrode-electrolyte region interfacial effects. To overcome these problems, a new technique based on the Gibbs Ensemble is proposed for simulation of an EDLC. In the proposed technique, each electrode is simulated in a separate simulation box. Application of periodic boundary conditions eliminates the interfacial effects. This in addition to the use of constant voltage ensemble allows for a more convenient comparison of simulation results with experimental measurements on typical EDLCs. (C) 2014 AIP Publishing LLC.

Active learning in keyword search-based data integration

The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: Global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword search-based data integration-where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data. A major open challenge is that under this model, the user only sees and offers feedback on a few ``top-'' results: This result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper, we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.

Comment on: Computation of the optical trapping force using an FDTD based technique

In this comment, problems associated with an oversimplified FDTD based model used for trapping force calculation in recent papers "Computation of the optical trapping force using an FDTD based technique" [Opt. Express 13, 3707 (2005)], and "Rigorous time domain simulation of momentum transfer between light and microscopic particles in optical trapping" [Opt. Express 12, 2220 (2004)] are discussed. A more rigorous model using in Poynting vector is also presented.

Voltage sensing buck converter-based technique for maximum photovoltaic solar energy delivery

A voltage sensing buck converter-based technique for maximum solar power delivery to a load is presented. While retaining the features and advantages of the incremental conductance algorithm, this technique is more desirable because of single sensor use. The technique operates by maximising power at the buck converter output instead of the input.

Linear algorithm and hexagonal search based two-pass algorithm for motion estimation

This paper presents a novel two-pass algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for block base motion compensation. On the basis of research from previous algorithms, especially an on-the-edge motion estimation algorithm called hexagonal search (HEXBS), we propose the LHMEA and the Two-Pass Algorithm (TPA). We introduced hashtable into video compression. In this paper we employ LHMEA for the first-pass search in all the Macroblocks (MB) in the picture. Motion Vectors (MV) are then generated from the first-pass and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of MBs. The evaluation of the algorithm considers the three important metrics being time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms, Experimental results show that the proposed algorithm can offer the same compression rate as the Full Search. LHMEA with TPA has significant improvement on HEXBS and shows a direction for improving other fast motion estimation algorithms, for example Diamond Search.