Intelligent front-ends and product models

Research efforts in two separate areas, intelligent user interfaces and product data models have in the past been carried out almost totally independently from each other. It would, however, seem that product models could be integrated into Intelligent Front-Ends and that results from Intelligent Front-End research could be used for building the user interfaces to database information stored according to product model schemas.The paper starts with an introduction to data base theory and then moves on to present general features of product models. The presentation is on a general level but the examples are picked from the building domain. The international effort to define a standardized basic product model framework, the PDES/STEP project, and the data modeling methods used in it are also described. Work with prototype interfaces to product data models for the building domain is described as an example of the role Intelligent Front-Ends could play in product model based applications. The paper ends with a short discussion of the role product models could have in Intelligent Front-Ends.     

Interactive ads recommendation with contextual search on product topic space

The rapid popularization of various online media services have attracted large amounts of consumers and shown us a large potential market of video advertising. In this paper, we propose interactive service recommendation based on ad concept hierarchy and contextual search. Instead of traditional ODP (Open Directory Project) based approach, we built a ad domain based concept hierarchy to make the most of the product details over the e-commerce sites. Firstly, we capture the summarization images related to the advertising product in the video content and search visually similar product images from the built product image database. Then, we aggregate the visual tags and textual tags with K-line clustering. Finally, we map them to the product concept space and make keywords suggestion, and users can interactively select keyframes or keywords to personalize their intentions by textual re-search. Experiments and comparison show that the system can accurately provide effective advertising suggestions.     

Large scale product search with spatial quantization and deep ranking

Multimedia Tools and Applications - Product image search aims to retrieve similar product images based on a query image. While deep learning based features work well in retrieving images of the...     

用于图像购物搜索的局部匹配算法

图像匹配是图像购物搜索的一个关键问题。针对现有的图像购物搜索网站利用底层特征无法进行局部匹配的问题,提出一种基于局部匹配的图像购物搜索方法,该方法允许用户自定义感兴趣区域,并利用SIFT（Scale Invariant Feature Transform）描述子提取该区域的视觉特征进行匹配。实验结果表明,该方法取得了较理想的预期效果,更好地满足了用户的实际购物搜索需求。     

An information-theoretic approach to consumer preference and product planning

Marketing managers should develop various marketing approaches in order to satisfy the needs of consumers. It is important for industries to succeed to analyze the data from consumers and to apply the quantitative analyses to marketing research. In this paper, a procedure for measuring the preferences weights of consumers for attribute levels of prototype products is proposed using paired comparison with scores. An information-theoretic procedure for measuring the consumer preference for each prototype product and the importance or utility of each attribute is presented, considering the preference weights for attribute levels as prior information. Furthermore, optimal product planning to maximize the total expected profit is discussed through numerical experiments.     

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P (http://smartp2p.cs.ucy.ac.cy/) system deployed over our SmartLab (http://smartlab.cs.ucy.ac.cy/) testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95 %, with one order of magnitude less time and two orders of magnitude less energy than its competitors.     

Intelligent search agents using web-driven natural-language explanatory dialogs

A computational linguistics approach for Web-based cooperative dialogs focuses on the user's requests by automatically generating language. driven interactions that take into account the context, user feedback, and the initial search's results.     

Effective product quantization-based indexing for nearest neighbor search

Multimedia Tools and Applications - Product quantization is a widely used lossy compression technique that can generate high quantization levels by a compact codebook set. It has been conducted in...     

Affective preference measurement of product appearance based on event-related potentials

The product appearance plays an important role in users’ purchase decision, while the emotions elicited by the product appearance are subtle, low intensity and not easy to obtain. Event-related potentials (ERPs) can reflect people’s psychological activities and effectively identify the characteristics of brain’s affective process. In the present study, the affective preference measurement of humidifier appearance was conducted based on the theory of affective design and event-related potentials. Firstly, the collected humidifier pictures were screened to be the experimental stimuli through multidimensional scaling analysis and cluster analysis. During the affective preference measurement experiment, the participants were asked to judge the affective preference level (liked vs. neutral vs. disliked) of humidifier pictures with the mouse button clicking (left, middle, right), and the electroencephalograph signals were recorded at the same time. ERPs results showed that the frontal and central N1 and frontal, frontal–central and central late positive potential can be taken as the indexes to measure participants’ affective preference of humidifier appearance. In conclusion, the participants’ affective preference level of humidifier appearance can be measured by ERPs. The experiment results can be used to guide product design, by measuring the brain activities of different prototypes at the phase of product development we can roughly infer which one is the user preferred or not preferred.     

Evaluating product search and recommender systems for E-commerce environments

Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper describes the design and implementation of two user studies in which a particular product search tool, known as example critiquing, was evaluated against a chosen baseline model. The results confirm that example critiquing significantly reduces users’ task time and error rate while increasing decision accuracy. Additionally, the results of the second user study show that a particular implementation of example critiquing also made users more confident about their choices. The main contribution is that through these two user studies, an evaluation framework of three criteria was successfully identified, which can be used for evaluating general product search and recommender systems in E-commerce environments. These two experiments and the actual procedures also shed light on some of the most important issues which need to be considered for evaluating such tools, such as the preparation of materials for evaluation, user task design, the context of evaluation, the criteria, the measures and the methodology of result analyses.     

Using online search data to forecast new product sales

Search engines are rapidly emerging to be the “go-to” sites for consumers to learn more about a product, concept or a term of interest, irrespective of the initial channel in which the interest originated — text, radio, TV, multi-media channels, word of mouth, etc. In this paper we argue that data on the search terms used by consumers can provide valuable measures and indicators of consumer interest in a product, concept or a term. Such data can be particularly valuable to managers in gauging potential product interest in a new product launch context or consumption interest in the post-release context. Based on this premise, we develop a model of pre-launch search activity and link the pre-launch search behavior and product characteristics to early sales of the product, thus providing a useful forecasting tool. Applying the model in the context of motion pictures, we find that search term usage follows rather predictable patterns in the pre-launch and post-launch periods and the model provides significant power in forecasting release week sales as a function of pre-release search activity. With advertising data included in the model, we find that the pre-release search data offers additional explanatory and forecasting power, thus highlighting the ability of the search data to capture other factors, such as possibly word-of-mouth, in impacting early sales. We offer specific insights into how managers can use search volume data and the model to plan their new product release.     

基于知识库系统的中文智能搜索引擎

随着信息技术的发展和信息量的增长,传统的搜索引擎技术日益不能满足用户信息查询的需要.目前.搜索引擎技术与人工智能(AI)技术的结合已经成为网络信息搜索的关键技术与核心思想.一种基于知识库系统的智能搜索引擎技术已成为当前研究的热点.主要介绍基于知识库系统的中文智能搜索引擎及其实现技术,以及中文智能搜索引擎的主要发展方向.     

基于分组模型的引力搜索智能大数据聚类方法

提出一种基于分组的引力搜索算法实现数据聚簇.与标准引力搜索不同,分组引力搜索设计一种特定的解编码策略,即分组编码,可将数据聚簇的相关结构映射为解的一部分;对于特定编码,新的引力搜索机制在位置和速度更新策略上设计适合分组编码的更新规则,使分组引力搜索可类似于传统引力搜索进行迭代寻优.在多种经典测试数据集下对算法性能进行评...     

Integrating preference analysis and balanced scorecard to product planning house of quality

Product planning house of quality (PPHOQ) is of fundamental and strategic importance in quality function deployment (QFD). Determining the aggregated priority ratings (PRs) of engineering characteristics (ECs) is a crucial step of constructing PPHOQ. A QFD team often involves a group of cross-functional team member from marketing, design, quality, plan, finance, logistics, and after service. Each member of the QFD team from different backgrounds often demonstrates significantly different behavior from the others and generates different preference assessment to the competitive products. The gross benefit of implementing the improvement goal of the performance value (IGPV) of each EC should be estimated from the short- and long-term perspectives in a comprehensive and accurate way. In this paper, we propose an extended PPHOQ by using a least deviation based approach (LDBA), and balanced scorecard (BSC), and develop a comprehensive and systematic approach to determine the aggregated PRs of ECs in the extended PPHOQ. The proposed approach is based on the presented LDBA for dealing with competition and preference information to determine the technical point of each EC, and the combination of BSC and analytic hierarchy process for estimating the gross benefit of implementing the IGPV of each EC. Finally, a case study is provided to illustrate the effectiveness of the proposed approach.     

Intelligent preference selection model based on NRE for evaluating student learning achievement

In order to evaluate student learning achievement, several aspects should be considered, such as exercises, examinations, and observations. Traditionally, such an evaluation calculates a final score using a weighted average method after awarding numerical scores, and then determines a grade according to a set of established crisp criteria. However, this approach lacks the potential to reflect the individual characteristics of a class compared to others. Several researches have used fuzzy techniques to devise practical methods for evaluating student learning achievement to ascertain linguistic terms that are usually used by teachers to assess student learning achievement. However, these approaches are largely based on expert opinions and require complicated computational processes. In this paper, we present a new method for evaluating student learning achievement using an adaptive ordered weighted averaging operator and K-nearest-neighbor classification method. The proposed method simulates the evaluation behavior of teachers when performing a student achievement evaluation based on a norm-referenced evaluation by identifying situations involving the application of intelligence and provides a useful means to award a reasonable grade to students. Furthermore, the proposed method provides a feedback mechanism to update the norm dataset. Therefore, the repetitious use of the feedback mechanism will gradually strengthen the representativeness of the norm dataset.     

Personalized product search based on user transaction history and hypergraph learning

Multimedia Tools and Applications - As the e-commerce shopping websites like Amazon become more and more popular, amounts of products spring up on the internet and bring great difficulties to...     

Agent based intelligent search framework for product information using ontology mapping

The Semantic Web and Web services provide many opportunities in various applications such as product search and comparison in electronic commerce. We implemented an intelligent meta-search and recommendation system for products through consideration of multiple attributes by using ontology mapping and Web services. Under the assumption that each shopping site offers product ontology and product search service with Web services, we proposed a meta-search framework to configure a customer’s search intent, make and dispatch proper queries to each shopping site, evaluate search results from shopping sites, and show the customer the relevant product list with associated rankings. Ontology mapping is used for generating proper queries for shopping sites that have different product categories. We also implemented our framework and performed empirical evaluation of our approach with two leading shopping sites in the world.     

ICADS: Intelligent Car Audio Design System for product planning

This paper describes the development of a system, ICADS (Intelligent Car Audio Design System), that can be used to effectively support product development engineers in car audio product design. We have demonstrated the use of expert system technology and the technique of quality function deployment (QFD) in supporting car audio design planning. QFD is applied as a knowledge acquisition method in this study to support car audio design teams in the development of products in a structured way that relates market demand via engineering specifications to parts specifications. We believe that other industries can use a similar approach for developing an expert system that can support product design planning.     

利用本体技术实现的智能搜索与录入系统

针对网络信息共享的需求和农业学科自身的特点,利用本体技术,提出基于网络农业信息分类编码的农业信息本体作为网页分类标识、数据库管理和搜索关键字推理的标准。拓展了传统搜索引擎的结构,结合Agent和Jena技术,构建了一个具有信息检索和自动归类的搜索与录入系统,使得系统更具智能化、人性化和专业化,有效地利用信息,提高了查全率和查准率。