Embedding Implicit User Importance for Group Recommendation

Group recommendations derive from a phenomenon in which people tend to participate in activities together regardless of whether they are online or in reality,which creates real scenarios and promotes the development of group recommendation systems.Different from traditional personalized recommendation methods,which are concerned only with the accuracy of recommendations for individuals,group recommendation is expected to balance the needs of multiple users.Building a proper model for a group of users to improve the quality of a recommended list and to achieve a better recommendation has become a large challenge for group recommendation applications.Existing studies often focus on explicit user characteristics,such as gender,occupation,and social status,to analyze the importance of users for modeling group preferences.However,it is usually difficult to obtain extra user information,especially for ad hoc groups.To this end,we design a novel entropy-based method that extracts users’implicit characteristics from users’historical ratings to obtain the weights of group members.These weights represent user importance so that we can obtain group preferences according to user weights and then model the group decision process to make a recommendation.We evaluate our method for the two metrics of recommendation relevance and overall ratings of recommended items.We compare our method to baselines,and experimental results show that our method achieves a significant improvement in group recommendation performance.

HGeoHashBase: an optimized storage model of spatial objects for location-based services

Many location-based services need to query objects existing in a specific space,such as location-based tourism resource recommendation.Both a large number of spatial objects and the real-time object access requirements of location-based services pose a big challenge for spatial object storage and query management.In this paper,we propose HGeoHashBase,an improved storage model by integrating GeoHash with key-value structure,to organize spatial objects for efficient range queries.GeoHash is responsible for spatial encoding and key-value structure as underlying data storage.Both the similarity of the encodings for objects in the close geographical locations and the multi-version data mechanism are blended into the proposed model well.Considering the tradeoff between encoding precision and query performance,a theoretical proof is presented.Extensive experiments are designed and conducted,whose results show that the proposed model can gain significant performance improvement.