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.

A Generative Model Approach for Geo-Social Group Recommendation

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem： 1） the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2） users have different influences when in diffe- rent groups. In this paper, we propose a generative geo-social group recommendation model （GSGR） to recommend points of interest （POIs） for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.

A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Due to the popularity of group activities in social media,group recommendation becomes increasingly significant.It aims to pursue a list of preferred items for a target group.Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users.However,these methods may suffer from data sparsity problem.Except for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and items.Such interactions,which take place in the range of a group,form a local view of a certain group.In addition to local information,groups with common interests may also show similar tastes on items.Therefore,group representation can be conducted according to the similarity among groups,which forms a global view of a certain group.In this paper,we propose a novel global and local information fusion neural network(GLIF)model for group recommendation.In GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local representation.Moreover,our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups.Then,it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation.Finally,group recommendation is conducted under neural collaborative filtering(NCF)framework.Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

RECOMMENDATIONS FROM THE HERBAL MEDICINES AND PRACTITIONERS WORKING GROUP IN THE UK: RAISING THE STANDARDS OF MANUFACTURED HERBAL PRODUCTS

The UK government set up a Herbal Medicines and Practitioners Working Group(HMPWG)to assess possibilities of:1.Setting up Statutory Regulation of Herbal Medicine Practitioners;2.Allowing continued public access to valuable herbal medicines.On 26th March 2015 the HMPWG published the key recommendations after over a year of meetings and consultation with experts.

GRIP： A Group Recommender Based on Interactive Preference Model

Numerous applications of recommender systems can provide us a tool to understand users. A group recommender reflects the analysis of multiple users＇ behavior, and aims to provide each user of the group with the things they involve according to users＇ preferences. Currently, most of the existing group recommenders ignore the interaction among the users. However, in the course of group activities, the interactive preferences will dramatically affect the success of recommenders. The problem becomes even more challenging when some unknown preferences of users are partly influenced by other users in the group. An interaction-based method named GRIP （Group Recommender Based on Interactive Preference） is presented which can use group activity history information and recommender post-rating feedback mechanism to generate interactive preference parameters. To evaluate the performance of the proposed method, it is compared with traditional collaborative filtering on the MovieLens dataset. The results indicate the superiority of the GRIP recommender for multi-users regarding both validity and accuracy.