Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape

Disgust and Distinction: The case of the jellied eel

Drawing on a series of ethnographic encounters collected while hanging around at a seafood stand in east London, the following article aims to explore the relationship between individual expressions of distaste and the production of class, ethnic and generational forms of distinction. Starting with the visceral expressions of distaste directed towards a seafood stand, the following paragraphs draw on a combination of historical and ethnographic data rendered through a matrix of anthropological, sociological and psychoanalytic theory, to explore the role of everyday ambient experiences and the stratifying processes that cut across the lives of the city's inhabitants. Arguing against purely biological explanations of disgust, the paper explores how social histories and cultural experience inflect gut responses to the sensoria that suffuse urban environments. Moving the focus beyond the social construction of urban sensibilities, the paper goes on to develop an account of culturally inflected forms of distaste, shaping the city and the lives of its inhabitants

Schematic Maps for Robot Navigation

An approach to high-level interaction with autonomous robots by means of schematic maps is outlined. Schematic maps are knowledge representation structures to encode qualitative spatial information about a physical environment. A scenario is presented in which robots rely on highlevel knowledge from perception and instruction to perform navigation tasks in a physical environment. The general problem of formally representing a physical environment for acting in it is discussed. A hybrid approach to knowledge and perception driven navigation is proposed. Different requirements for local and global spatial information are noted. Different types of spatial representations for spatial knowledge are contrasted. The advantages of high-level / low-resolution knowledge are pointed out. Creation and use of schematic maps are discussed. A navigation example is presented