Text recommendation based on time series and multi-label information

Abstract

One of the key functions of the method of text recommendation is to build a correlation analysis to all the text collection. At present, most of the text recommendation methods use the citation network, but less to consider the internal relations, which has become a challenge and an opportunity for the research of text recommendation. Therefore, we propose a new method to ameliorate the above problem based on the time series in this paper. We specify a certain text collection according to the interests of users and integrate the varied label values of the text, then we build the correlation coefficient between text and its related text with the differential analysis, finally the similarity degree of the text is calculated out by using the improved cosine similarity correlation matrix to promote a recommendation of similar text. Our experiments indicate that we are able to ensure the quality of text, with an improvement of accuracy by 8.63% as well as an improvement of recall rate by 5.25%.

Alternative CPU and GPU Parallel Computing Approaches for Improving Sequential Analysis of Probability Associations in Short Texts

“…Alagoz [11] suggested a deep relational similarity analysis which explores path probabilities between words by utilizing power of probabilistic relation matrix. More recently, Y. Yin et al [15] introduced a method to improve accuracy of text recommendation by 8.63%. The method in [15] utilizes improved cosine similarity measure to compare correlation coefficients vectors of related texts.…”

“…More recently, Y. Yin et al [15] introduced a method to improve accuracy of text recommendation by 8.63%. The method in [15] utilizes improved cosine similarity measure to compare correlation coefficients vectors of related texts. Moreover, in an attempt to minimize the computational cost, Mikolov et al [16] presented the Skip-gram Model which utilizes probability to predict surrounding words in a short text.…”