An Empirical Study of SLDA for Information Retrieval

Ma, Dashun; Lan, Rao; Wang, Ting

doi:10.1007/978-3-642-25631-8_8

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…(1). reflects the relative strength between the implicit topics in the document collection, denotes all implicit probability distribution containing topic itself, the process of generating the document are as follows [11].…”

Section: Introduction To Lda Modelmentioning

confidence: 99%

Question Recommendation Mechanism under Q&A Community based on LDA Model

Wang¹,

Lin²,

Yang³

2014

TOCSJ

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Aiming at the questions not answered timely under Q&A community, a kind of questions recommendation method based on LDA (Latent Dirichlet Allocation) topic model is proposed, which fully utilizes personalized information of users under Q&A community. The interests distributions of users are expressed through using LDA model and according to the interests distributions of users, questions recommendation lists are calculated out at last. The proposed method can recommend the unsolved problems to users who are interested in these questions, which makes these questions be solved out as soon as possible, and promotes information dissemination and knowledge sharing under Q&A community. Experimental results show that the proposed questions recommendation method based on LDA not only discoveries the unsolved questions quickly, but also recommends the most suitable answers to users compared with PLSA, KL-divergence and Cosine similarity.

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Section: Introduction To Lda Modelmentioning

confidence: 99%