Vidinli, I.B.; Ozcan, R.: New query suggestion framework and algorithms : a case study for an educational search engine (2016)
0.01
0.0065180818 = product of:
0.016295204 = sum of:
0.01155891 = weight(_text_:a in 3185) [ClassicSimilarity], result of:
0.01155891 = score(doc=3185,freq=16.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.2161963 = fieldWeight in 3185, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=3185)
0.0047362936 = product of:
0.009472587 = sum of:
0.009472587 = weight(_text_:information in 3185) [ClassicSimilarity], result of:
0.009472587 = score(doc=3185,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.116372846 = fieldWeight in 3185, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=3185)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as "comparison of queries". We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66-90% statistically significant increase in relevance of query suggestions compared to a baseline method.
- Source
- Information processing and management. 52(2016) no.5, S.733-752
- Type
- a