信息检索用户实验设计中时间限制和任务次序的影响研究

[目的/意义] 通过分析时间限制和任务次序在用户实验设计中对被试和实验结果的影响,提出响应的优化方案。[方法/过程] 使用实验法,对被试搜索时间进行严格的控制,分别验证时间限制和任务次序对被试搜索体验的影响。[结果/结论] 在有时间限制的情况下,被试对于成功完成搜索任务的信心较低,且在任务结束后,对检索结果的自我认同度较低;任务次序也会影响被试的检索体验,搜索任务的次序越靠前,用户的信心和体验越易受到较为负面的影响。建议学者在采用实验法进行用户行为分析的时候,要科学全面地对搜索任务和情境变量进行轮换,如采用拉丁方的设计。同时,若非检验实验时间限制对搜索行为的影响,应尽量避免实验过程中对被试的搜索时间进行限制。     

Bridging memory-based collaborative filtering and text retrieval

When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users’ preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user’s ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering.     

信息过滤研究

提出了一种新的数字图书馆信息过滤方法，它具有三个显著的优点：一是采用了混合信息过滤模型，克服了基于内容和协作过滤的不足；二是建立用户模板，解决了用户兴趣的获取问题；三是信息内容采用本体来组织，实现语义级查询和高效的匹配机制。     

基于层次任务分析的社交网站信息构建评估 ——以花瓣网为例

在综合比较三种网站信息构建评估方法的基础上,采用层次任务分析与用户测试相结合的方法对社交网站的信息构建进行系统分析与评估,以社交分享类网站——花瓣网为例:首先构建花瓣网层次任务图与行动计划,然后进行用户任务测试,使用4种度量指标对测试数据进行标准化处理,最后得出网站信息构建评估分数,发现该网站信息构建中存在的问题。     

智能信息检索中个性化模式的表示形式研究

智能信息检索中 ,个性化模式的描述和更新决定了文档过滤的效率。本文根据Huffman树的特点 ,提出基于Huffman树形式组织用户个性化模式并给出其相应的文档过滤算法。与其他他同的个性化模式过滤算法的性能比较而言 ,其具有占用空间少 ,过滤速度快的优点。     

一个新的基于协作过滤的用户浏览预测模型

本文提出了一个新的基于协作过滤的用户浏览协作预测模型———UNCPM ,它有效地解决了目前协作过滤预测方法的准确性和覆盖率低等问题。UNCPM从Web日志中获取用户浏览信息 ,系统分为两个部分 :离线构件和在线构件。离线构件用于用户浏览历史记录的K means聚类 ,并在聚类时充分考虑URL的相似分析来避免协作过滤的同义性和分散性等不足 ;在线构件用于活动用户预测。该模型可以应用在大型电子商务网站的用户浏览预测上。     

一种基于Rough集理论的最近邻协同过滤算法

目前协同过滤被广泛应用于数字图书馆、电子商务等领域的个性化服务系统.最近邻算法则是最早提出和最主要的协同过滤推荐算法,但用户评分数据稀疏性严重影响推荐质量.针对上述问题,提出了一种基于Rough集理论的最近邻协同过滤算法,以用户评分项并集作为用户相似性计算基础,并将非目标用户区分为无推荐能力和有推荐能力两种类型;对于无推荐能力用户不再计算用户相似性以改善推荐实时性,对于有推荐能力用户则提出一种基于Rough集理论的评分预测方法来填补用户评分项并集中的缺失值,从而降低数据稀疏性.实验结果表明新算法能有效提高推荐质量.     

Improving digital skills for the use of online public information and services

There are recent indications regarding the use of online public services that force the government to focus on the more refined conceptualizations digital divide research has produced. This paper addresses one of the factors that appears to be important in several conceptualizations of how to approach the digital divide; the differential possession of so-called digital skills. The problem of being short of skills becomes urgent when governments suppose that citizens are able to complete about every task on the Internet. Operational definitions for operational, formal, information and strategic skills are used to measure the Internet skills of the Dutch population at large, by giving 109 subjects nine government related assignments to be accomplished on the Internet. Subjects were recruited following a two step approach; randomly select a sample from the book/list of fixed telephony subscribers, followed by drawing a selective quota sample for the strata of gender, age and educational level. The results indicate that on average 80% of the operational skill Internet tasks, 72% of formal Internet skills tasks, 62% of the information Internet skills tasks and 22% of strategic Internet skills tasks assigned have been successfully completed. The Dutch government's expectation that every citizen with an Internet connection is able to complete the assignments following tasks the government thinks every Internet user can perform, clearly is not justified. The article provides two types of policy recommendations to change this state of affairs. Recommendations for improving government websites and for improving the skill levels of Dutch citizens are suggested.     

A Critique of the FRBR User Tasks and Their Modifications

The four FRBR user tasks have become widely accepted as functions of the library catalog, but there have been only sporadic discussions concerning their validity and sufficiency, despite their modification in the models subsequently presented in the FRAD, FRSAD, and draft FRBR-LRM reports. This article presents a critique of the four variant sets of user tasks, and proposes an extended set of six generic end-user tasks, applicable to both bibliographic and authority data: locate, collocate, connect, identify, select, and obtain. The article also outlines their interrelationships and suggests those tasks that may be particularly well supported by professional cataloging.     

基于属性值偏好矩阵的协同过滤推荐算法

传统的协同过滤推荐算法面临用户评分数据稀疏性和冷启动问题的挑战.针对上述问题,提出了基于属性值偏好矩阵的协同过滤推荐算法,首先采用奇异值分解(SVD)对用户-项目评分矩阵降维得到目标用户的初始邻居用户集,生成新的用户-项目评分矩阵;然后将用户评分映射到相应的项目属性值上,生成每个用户的属性值偏好矩阵,并基于属性值偏好矩阵进行用户相似性度量,从而缓解了评分数据稀疏性;将新项目的属性值与用户的属性值偏好矩阵进行匹配,从而找出匹配度最高的前N个用户作为新项目的推荐受众.实验结果表明了该算法的有效性.     

Multilingual Information Access (MLIA) Tools on Google and WorldCat: Bi/Multilingual University Students’ Experience and Perceptions

Abstract

This article reports on the results of an exploratory user-centered study that examined how technological advancements in natural language processing (NLP) such as the availability of multilingual information access (MLIA) tools impact the information searching behavior of bi/multilingual academic users. Thirty-one bi/multilingual students participated in a controlled lab-based user experiment in which they carried out two assigned tasks each on Google and WorldCat for a total of four tasks, and then completed a post experiment questionnaire. The captures from the experiment showed 86.7% of the participants using multilingual information access tools. Further analyses of the captures also showed that participants were more likely to use MLIA tools when the instructions for the task were stated in their native language. An independent samples t-test revealed that participants spent less time on their searches when they used MLIA tools. The study revealed considerable diversity in the information searching behavior of the participants, even within the same pair of languages, and even for the same user. Diversity was noted for instance, on which tasks MLIA tools were used and in how these tools were used. User-centered designed, personalized multilingual information retrieval (PMLIR) models could hold promise for best representing the information searching behavior of bi/multilingual users.     

一种基于情景的多维协同过滤新方法研究

介绍传统协同过滤方法,提出一个新的基于情景的多维协同过滤推荐模型。在该模型中,介绍情景的概念;阐述建立基于情景的多维用户模型的方法,并对基于情景的多维协同过滤推荐模型的组成部分进行详细介绍。提出一种计算情景相似度的新算法。基于该新算法,可以得到当前用户的“最近邻”在当前用户所在情景下对项目的评分。     

Analysis of FRBR User Tasks

FRBR, FRAD, and FRSAD models propose user tasks as a way to address and categorize functions that a catalog should support. The user tasks are not harmonized among these models, but to do that, they should first be fully understood and analyzed, especially “select” and “identify.” We decided to look at the FRBR user tasks from the perspective of interactive information retrieval (IIR). Several IIR models were reviewed and Ellis’ and Belkin's models were chosen for further analysis and interpretation of FRBR “select” and “identify” tasks.     

基于Ontology的文档过滤研究

区分文档过滤、信息过滤和文本过滤并介绍文档过滤技术的研究现状；提出基于Ontology的文档过滤的设想，认为其优势在于灵活、共享性好、有利于进行个性化服务等；讨论基于Ontology的文档过滤的实施过程，包括构建准备、本体构建、本体调用，重点阐述公共本体、用户本体和文档本体的构建方法以及实施过程中涉及的技术体系；最后指出今后的努力方向。     

Detection As Multi-Topic Tracking

The topic tracking task from TDT is a variant of information filtering tasks that focuses on event-based topics in streams of broadcast news. In this study, we compare tracking to another TDT task, detection, which has the goal of partitioning all arriving news into topics, regardless of whether the topics are of interest to anyone, and even when a new topic appears that had not been previous anticipated. There are clear relationships between the two tasks (under some assumptions, a perfect tracking system could solve the detection problem), but they are evaluated quite differently. We describe the two tasks and discuss their similarities. We show how viewing detection as a form of multi-topic parallel tracking can illuminate the performance tradeoffs of detection over tracking.     

Probabilistic relevance ranking for collaborative filtering

Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice we may only have implicit evidence of user preference; and furthermore, a better view of the task is of generating a top-N list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly cast as a relevance ranking problem. We begin with the classic Probability Ranking Principle of information retrieval, proposing a probabilistic item ranking framework. In the framework, we derive two different ranking models, showing that despite their common origin, different factorizations reflect two distinctive ways to approach item ranking. For the model estimations, we limit our discussions to implicit user preference data, and adopt an approximation method introduced in the classic text retrieval model (i.e. the Okapi BM25 formula) to effectively decouple frequency counts and presence/absence counts in the preference data. Furthermore, we extend the basic formula by proposing the Bayesian inference to estimate the probability of relevance (and non-relevance), which largely alleviates the data sparsity problem. Apart from a theoretical contribution, our experiments on real data sets demonstrate that the proposed methods perform significantly better than other strong baselines.

Holistic schedulability analysis for distributed hard real-time systems

This paper extends the current analysis associated with static priority pre-emptive based scheduling to address the wider problem of analysing schedulability of a distributed hard real-time system; in particular it derives analysis for a distributed system where tasks with arbitrary deadlines communicate by message passing and shared data areas. A simple TDMA protocol is assumed, and analysis developed to bound not only the communications delays, but also the delays and overheads incurred when messages are processed by the protocol stack at the destination processor. The paper illustrates how a window-based analysis technique can be used to find the worst-case response times of a distributed task set. An extended example illustrating the application of the analysis is presented.