Mining consumer product data via latent semantic indexing☆

Extended Latent Class Models for Collaborative Recommendation

“…For example, customer segmentation can be readily achieved via the use of cluster models. Various methods have been proposed in the literature, including cluster models [15], dependency models [18], classifer models [19] and subspace methods [12].…”

“…As the approach does not rely on product contents, it does not possess the two problems of the content-based approach and thus has widely been used for recommending products where product descriptions are either lacking or found to be too specific to be useful. Many different techniques have been proposed for collaborative recommendation, including the most original correlation-based methods [9], [10], latent semantic indexing (LSI) [11], [12], Bayesian learning [13], [14], etc. Successful application domains include recommendation of Usenet articles [9], musics [10], etc.…”

Protecting research and technology from espionage

“…Semantic relationship between terms can be identified by computational techniques that use statistical procedures on eigenvectors (Jiang, Berry, Donato, Ostrouchov, & Grady, 1999;Luo, Chen, & Xiong, 2011). These techniques consider words that are in a project description as well as words that might be in these descriptions (Thorleuchter & Van den Poel, 2012c;Thorleuchter & Van den Poel, 2012d;).…”

Data mining

“…Moreover, DLs often have keywords or other attributes. This suggests concept clustering (Grossman, 1996) by term or latent semantic indexing (Jiang, Berry, Donato, Ostouchov, & Grady, 1999), or association queries for attribute‐based associations (Abiteboul, 1997).…”