-
Wang, H.; Wang, C.: Ontologies for universal information systems (1995)
0.01
0.014347555 = product of:
0.02869511 = sum of:
0.0132912 = weight(_text_:a in 3194) [ClassicSimilarity], result of:
0.0132912 = score(doc=3194,freq=10.0), product of:
0.05832264 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.05058132 = queryNorm
0.22789092 = fieldWeight in 3194, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=3194)
0.01540391 = product of:
0.03080782 = sum of:
0.03080782 = weight(_text_:information in 3194) [ClassicSimilarity], result of:
0.03080782 = score(doc=3194,freq=10.0), product of:
0.088794395 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.05058132 = queryNorm
0.3469568 = fieldWeight in 3194, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=3194)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- The increasing complexity of problems addressed by massively distributed information systems has led to the development of worldwide information systems, such as the WWW and Hyper-G. Presents a conceptual framework for those systems using a knowledge representation language, Telos, by concentrating on the semantics of their domains. The framework is developed and based on careful analysis and abstraction of those existing systems. By creating a rich and precise conceptual framework, provides a foundation for the specification and implementation of future universal information systems.
- Source
- Journal of information science. 21(1995) no.3, S.232-239
- Type
- a
-
Liu, Z.; Wang, C.: Mapping interdisciplinarity in demography : a journal network analysis (2005)
0.01
0.013383064 = product of:
0.026766129 = sum of:
0.014710663 = weight(_text_:a in 4384) [ClassicSimilarity], result of:
0.014710663 = score(doc=4384,freq=4.0), product of:
0.05832264 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.05058132 = queryNorm
0.25222903 = fieldWeight in 4384, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.109375 = fieldNorm(doc=4384)
0.012055466 = product of:
0.024110932 = sum of:
0.024110932 = weight(_text_:information in 4384) [ClassicSimilarity], result of:
0.024110932 = score(doc=4384,freq=2.0), product of:
0.088794395 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.05058132 = queryNorm
0.27153665 = fieldWeight in 4384, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.109375 = fieldNorm(doc=4384)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Source
- Journal of information science. 31(2005) no.4, S.308-316
- Type
- a
-
Wang, C.: Bibliometrics : a textbook (1990)
0.01
0.009624632 = product of:
0.019249264 = sum of:
0.008916007 = weight(_text_:a in 5040) [ClassicSimilarity], result of:
0.008916007 = score(doc=5040,freq=2.0), product of:
0.05832264 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.05058132 = queryNorm
0.15287387 = fieldWeight in 5040, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.09375 = fieldNorm(doc=5040)
0.010333257 = product of:
0.020666514 = sum of:
0.020666514 = weight(_text_:information in 5040) [ClassicSimilarity], result of:
0.020666514 = score(doc=5040,freq=2.0), product of:
0.088794395 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.05058132 = queryNorm
0.23274569 = fieldWeight in 5040, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.09375 = fieldNorm(doc=5040)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Footnote
- Rez. in: Journal of information, communication, and library science. 2(1995) no.2, S.84-85 (J. Qin)
-
Wang, C.; Zhao, S.; Kalra, A.; Borcea, C.; Chen, Y.: Predictive models and analysis for webpage depth-level dwell time (2018)
0.01
0.009220011 = product of:
0.018440021 = sum of:
0.0098289745 = weight(_text_:a in 4370) [ClassicSimilarity], result of:
0.0098289745 = score(doc=4370,freq=14.0), product of:
0.05832264 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.05058132 = queryNorm
0.1685276 = fieldWeight in 4370, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=4370)
0.008611047 = product of:
0.017222093 = sum of:
0.017222093 = weight(_text_:information in 4370) [ClassicSimilarity], result of:
0.017222093 = score(doc=4370,freq=8.0), product of:
0.088794395 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.05058132 = queryNorm
0.19395474 = fieldWeight in 4370, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=4370)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- A half of online display ads are not rendered viewable because the users do not scroll deep enough or spend sufficient time at the page depth where the ads are placed. In order to increase the marketing efficiency and ad effectiveness, there is a strong demand for viewability prediction from both advertisers and publishers. This paper aims to predict the dwell time for a given urn:x-wiley:23301635:media:asi24025:asi24025-math-0001 triplet based on historic data collected by publishers. This problem is difficult because of user behavior variability and data sparsity. To solve it, we propose predictive models based on Factorization Machines and Field-aware Factorization Machines in order to overcome the data sparsity issue and provide flexibility to add auxiliary information such as the visible area of a user's browser. In addition, we leverage the prior dwell time behavior of the user within the current page view, that is, time series information, to further improve the proposed models. Experimental results using data from a large web publisher demonstrate that the proposed models outperform comparison models. Also, the results show that adding time series information further improves the performance.
- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.8, S.1007-1022
- Type
- a
-
Ackerman, B.; Wang, C.; Chen, Y.: ¬A session-specific opportunity cost model for rank-oriented recommendation (2018)
0.01
0.008637558 = product of:
0.017275115 = sum of:
0.0099684 = weight(_text_:a in 4468) [ClassicSimilarity], result of:
0.0099684 = score(doc=4468,freq=10.0), product of:
0.05832264 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.05058132 = queryNorm
0.1709182 = fieldWeight in 4468, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=4468)
0.0073067155 = product of:
0.014613431 = sum of:
0.014613431 = weight(_text_:information in 4468) [ClassicSimilarity], result of:
0.014613431 = score(doc=4468,freq=4.0), product of:
0.088794395 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.05058132 = queryNorm
0.16457605 = fieldWeight in 4468, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=4468)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- Recommender systems are changing the way that people find information, products, and even other people. This paper studies the problem of leveraging the context of the items presented to the user in a user/system interaction session to improve the recommender system's ranking prediction. We propose a novel model that incorporates the opportunity cost of giving up the other items in the session and computes session-specific relevance values for items for context-aware recommendation. The model can work on a variety of different problems settings with emphasis on implicit user feedback as it supports varying levels of ordinal relevance. Experimental evaluation demonstrates the advantages of our new model with respect to the ranking quality.
- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.10, S.1259-1270
- Type
- a
-
Wang, C.; Zhou, R.; Lee, M.K.O.: Can loyalty be pursued and achieved? : an extended RFD model to understand and predict user loyalty to mobile apps (2021)
0.01
0.005370049 = product of:
0.010740098 = sum of:
0.0064345747 = weight(_text_:a in 259) [ClassicSimilarity], result of:
0.0064345747 = score(doc=259,freq=6.0), product of:
0.05832264 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.05058132 = queryNorm
0.11032722 = fieldWeight in 259, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=259)
0.0043055234 = product of:
0.008611047 = sum of:
0.008611047 = weight(_text_:information in 259) [ClassicSimilarity], result of:
0.008611047 = score(doc=259,freq=2.0), product of:
0.088794395 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.05058132 = queryNorm
0.09697737 = fieldWeight in 259, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=259)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- Although millions of mobile apps have been published in the app store, the majority are seldom downloaded or used. This phenomenon has intensified the competition among service providers for user loyalty. There were plenty of studies investigating user loyalty in the mobile-app context; nevertheless, most failed to identify those true loyalty users who embraced attitudinal and behavioral loyalty. To address this research gap, this study aims to understand and predict user loyalty by an extended RFD model. We propose that recency, frequency, and duration are able to reflect behavioral loyalty, while category frequency rate and category duration rate are representations of attitudinal loyalty. Using the actual data collected from a third-party app, we calculate the weights of each variable through the entropy weight method, evaluate users' loyalty in two dimensions, and classify users into four groups (i.e., true loyalty, latent loyalty, moderate loyalty, and no loyalty). We also conduct a dynamic analysis to investigate how users move across different loyalty conditions. The results indicate that majority of users tend to stay on their initial loyalty conditions. For those who have changed their loyalty conditions, it is found that true loyalty users are more likely to become latent loyalty users.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.7, S.824-838
- Type
- a
-
Wang, C.: ¬The online catalogue, subject access and user reactions : a review (1985)
0.00
0.004203046 = product of:
0.016812185 = sum of:
0.016812185 = weight(_text_:a in 986) [ClassicSimilarity], result of:
0.016812185 = score(doc=986,freq=4.0), product of:
0.05832264 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.05058132 = queryNorm
0.28826174 = fieldWeight in 986, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.125 = fieldNorm(doc=986)
0.25 = coord(1/4)
- Type
- a
