Review on Information Retrieval for Desktop Search Engine

Search is an important aspect of information management often taken for granted. Domain specific repositories are growing in both size and numbers calling for efficient search and retrieval of documents. This paper explores the possible techniques and necessary system components for a search engine charting several iterative optimizations over the last few years. This paper focuses on NLP models while retaining basic principles from other methods that assist in information search.

Modeling concepts and their relationships for corpus-based query auto-completion

Query auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestions. However, when query logs are not available, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach.

Review of empirical arguments on contractor pre-qualification criteria

Purpose The studies on contractor prequalification focus more on the review of models and algorithms rather than review of the criteria for contractor prequalification. However, the basis of every prequalification model primarily relates to the measurement and judgement of prospective contractors based on a set of decision criteria. This paper aims to address the gap by reviewing academic papers on contractor prequalification criteria. Design/methodology/approach A desktop search was conducted under the “T/A/K (title/abstract/keyword)” field of the Scopus search engine. A total of 49 papers were initially identified; however, only peer reviewed journals were selected for the study; therefore, a sample of 36 was subsequently used. Further filtering was done in which 26 papers were found valid for further analysis as it was realized that, not all the identified papers presented empirical arguments about the issue of contractor pre-qualification criteria. The selected 26 papers were subjected to content analysis to identify the key contractor pre-qualification criteria. Findings A total of 41 criteria were identified which were subsequently classified into six main categories, namely, technical considerations, management considerations, financial considerations, reputation considerations, general experience considerations and health, safety and environmental considerations. There was an indication that, the involvement of health, safety and environmental considerations in contractor prequalification proceedings is limited. Research limitations/implications The major limitation of this research was the limited number of papers selected for further analysis based on the Scopus search engine. The identified criteria serve as a basis for further empirical studies on contractor prequalification criteria. Practical implications The outcome of this study broadens the understanding of practitioners and researchers on the various criteria for contractor prequalification. Originality/value By critically reviewing available literature on contractor prequalification, the study sets the tone for further empirical studies on contractor prequalification.

The influence of snippet length on user behavior in mobile web search

Purpose Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search. Design/methodology/approach For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks. Findings Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries. Originality/value Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.

The Search Alternative

The two main retrieval strategies for accessing personal information are navigation and search. Critics of navigation point out that information is hidden from sight in folders that are often within other folders; so people have to remember the exact location of information in order to access it. Despite these arguments, several studies show that search is not the main way that people actually access their files. Instead people generally prefer to manually navigate to information rather than using desktop search. This preference is independent of the quality of the search engine used, and improved search engines do not reduce the extent to which people actively organize their information. Except when finding new web information, people use search only as a last resort when accessing personal files.