WEEK 8 READING NOTES

"In user-centered design, decisions are made based on responses obtained from target users of the system. (This is in contrast with standard software practice in which the designers assume they know what users need, and so write the code first and assess it with users later.) In user-centered design, first a needs assessment is performed in which the designers investigate who the users are, what their goals are, and what tasks they have to complete in order to achieve those goals. The next stage is a task analysis in which the designers characterize which steps the users need to take to complete their tasks, decide which user goals they will attempt to support, and then create scenarios which exemplify these tasks being executed by the target user population (Kuniavsky, 2003, Mayhew, 1999). "

The first step to do user-centered design is to define the target users group. Once getting known about the target users, analyzers could conclude their requirements and than help developers find proper way to design several functions to meet the requirements. The target users and the design goal are the fundermental factors of user-centered design. Thus, it is necessary to focus on this part. Without concentrating a lot on this part, the product might be useless or unsatified for users. This kind of situation is not what we want to see. Analyzing the requirements is an important step too. It requires analyzers to be experienced and working efficiently. By combining the target users group and requirement analysis, the design goal could be clear for designers to do further works.

WEEK 7 MUDDIEST POINT

How to evaluate the usability of relevance feedback?

WEEK 7 READING NOTES

"This work inspires several future directions. First, we can study a more principled way to model multiple negative models and use these multiple negative models to conduct constrained query expansion, for example, avoiding terms which are in negative models. Second, we are interested in a learning framework which can utilize both a little positive information (original queries) and a certain
amount of negative information to learn a ranking function to help dif cult queries. Third, queries are dif cult due to different reasons. Identifying these reasons and customizing negative feedback strategies would be much worth studying."

Collecting feedback is a very important step during the evaluation. According to those feedback, developers could get the direct ideas from users. Those ideas could help the developing department focus more on users' requirements than areas that developers thought important but users did not. Positive feedbacks always highlight the advantages of the system. However, frome negative feedbacks, analyzers could get better known about users' opinions. That is why the negative feeadback based model could be more useful than positive based model. By comparing all the negative feedback through negative model, developers could get an overview about the disadvantages of the system. Thus, further development of negative model is needed.

WEEK 6 MUDDIEST POINT

Given the fact that precision and recall cannot be fulfilled at the same time, so how to decide which one is better to be focused on while doing the sepecific retrieving?

WEEK 6 READING NOTES

"This sort of thing is extremely hard. But I do not believe that we should therefore not attempt to do it or argue, in a supposedly more principled manner, that setups within which modern retrieval systems have to operate are so di use, or so variegated, that it is a fundamental mistake to address anything but the immediate D * Q * R environment from which solid, transportable, general-purpose retrieval system knowhow can be acquired. In fact, indeed, TREC's newer tracks subvert both of these arguments: even if the lawyers' interpretation of \relevant" as referencing might be inferable from assessment data samples, one feels rather less con dent about being able to infer, even with the best modern machine learning tools, that the name of the retrieval game is getting information that \appears reasonably calculated to lead to the discovery of admissible evidence"."

The development of TREC has faced several challenges by far. Some people may think that it is not necessary to spend too much time and money on the related research. However, it is unfair to think this way. What TREC could bring to us is far more than our imagination. TREC track has finished several significant tasks in information retrieval field. It helps developers deal with huge amount of data more efficiently and accurately. And the standard of TREC file makes random data more standardized and more recognizable. One of the biggest challenges of TREC is that it is hard to convert huge data into TREC. It might cost too much time and machines to reach the goal. Thus, the further development of TREC is absolutely needed. And the research is definitely valuable.

WEEK 5 MUDDIEST POINT

If the probability of the query term in a document is very close to 1, the document is very possible a useless document. Is there a proper range about the probability to return the proper results without this kind extreme situation?

WEEK 5 READING NOTES

"The model decomposes into two parts: a document collection network and a query network. The document collection network is large, but can be precomputed: it maps from documents to terms to concepts. The concepts are a thesaurus-based expansion of the terms appearing in the document. The query network is relatively small but a new network needs to be built each time a query comes in, and then attached to the document network. The query network maps from query terms, to query subexpressions (built using probabilistic or ``noisy'' versions of AND and OR operators), to the user's information need. "

This kind of network is very useful in IR field. It helps the system to gather related information from the word. Once the network constructed, the whole could be retrieved. However, the adding of new network may bring problems for the system because of the storage and the speed. The network represents the boolean module and Probabilistic information retrieval. But the usage of them is not practical enough. The evalution of every models never stops. Thus, the development of construction of the network could based on newly developed models not only previous models. Also, the potential problems are needed to be noticed by developers too.

WEEK 4 MUDDIEST POINT

While calculating the weight of terms in the documents, how to identify the meaning of the words which may have several meanings because sometimes the different meaning word may have different weight, such as apple? Is there any standards for this kind of identification?