Topological analysis of the experimental electron density &(r) in hydrogen-bonding regions has been carried out for a large number of organic compounds using different multipole models and techniques. Relevant systematic relationships between topological properties at the critical points and the usual geometric parameters are pointed out. Results involving X-ray data only and joint X-ray and neutron data, as well as special hydrogen bonding cases (symmetric, bifurcated, peptide bonds, etc.) are included and analysed in the same framework. A new classi®cation of hydrogen bonds using the positive curvature of the electron density at the critical point ! 3 r CP is proposed.
Abstract-This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS, on the other hand, supports a taxonomy of three novel classes of locationbased ratings, namely, spatial ratings for non-spatial items, nonspatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the MovieLens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
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