Search (3 results, page 1 of 1)

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Abstract

A non-negligible fraction of user queries end up with very few or even no matching results in leading commercial web search engines. In this work, we provide a detailed characterization of such queries and show that search engines try to improve such queries by showing the results of related queries. Through a user study, we show that these query suggestions are usually perceived as relevant. Also, through a query log analysis, we show that the users are dissatisfied after submitting a query that match no results at least 88.5% of the time. As a first step towards solving these no-answer queries, we devised a large number of features that can be used to identify such queries and built machine-learning models. These models can be useful for scenarios such as the mobile- or meta-search, where identifying a query that will retrieve no results at the client device (i.e., even before submitting it to the search engine) may yield gains in terms of the bandwidth usage, power consumption, and/or monetary costs. Experiments over query logs indicate that, despite the heavy skew in class sizes, our models achieve good prediction quality, with accuracy (in terms of area under the curve) up to 0.95.

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Abstract

Daily deals have emerged in the last three years as a successful form of online advertising. The downside of this success is that users are increasingly overloaded by the many thousands of deals offered each day by dozens of deal providers and aggregators. The challenge is thus offering the right deals to the right users i.e., the relevance ranking of deals. This is the problem we address in our paper. Exploiting the characteristics of deals data, we propose a combination of a term- and a concept-based retrieval model that closes the semantic gap between queries and documents expanding both of them with category information. The method consistently outperforms state-of-the-art methods based on term-matching alone and existing approaches for ad classification and ranking.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

Temporal aspects have been receiving a great deal of interest in Information Retrieval and related fields. Although previous studies have proposed, designed and implemented temporal-aware systems and solutions, understanding of people's temporal information searching behaviour is still limited. This paper reports the findings of a user study that explored temporal information searching behaviour and strategies in a laboratory setting. Information needs were grouped into three temporal classes (Past, Recency, and Future) to systematically study their characteristics. The main findings of our experiment are as follows. (1) It is intuitive for people to augment topical keywords with temporal expressions such as history, recent, or future as a tactic of temporal search. (2) However, such queries produce mixed results and the success of query reformulations appears to depend on topics to a large extent. (3) Search engine interfaces should detect temporal information needs to trigger the display of temporal search options. (4) Finding a relevant Wikipedia page or similar summary page is a popular starting point of past information needs. (5) Current search engines do a good job for information needs related to recent events, but more work is needed for past and future tasks. (6) Participants found it most difficult to find future information. Searching for domain experts was a key tactic in Future search, and file types of relevant documents are different from other temporal classes. Overall, the comparison of search across temporal classes indicated that Future search was the most difficult and the least successful followed by the search for the Past and then for Recency information. This paper discusses the implications of these findings on the design of future temporal IR systems

Footnote

Beitrag in einem Themenschwerpunkt "Time and information retrieval"