Both papers “Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context” and “Forgetting the Words but Remembering the Meaning: Modelling Forgetting in a Verbal and Semantic Tag Recommender” give insights, how algorithms for suggesting tags through models, that describe the human categorization and memory process can be improved. The third paper “Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces” deals with how products and product categories in online market places, like e.g. Amazon through social networks and location data can be suggested more targeted and more efficient.
Read here the full papers:
Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context