A Group Recommendation Model Using Diversification Techniques

Financial Product Recommendation through Case-based Reasoning and Diversification Techniques

This work proposes a framework for financial product recommendation which combines case-based reasoning with diversification techniques to support wealth managers in recommending personalized investment portfolios. The performance of the framework has been evaluated against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed ...

A Single-Step Approach to Recommendation Diversification

This paper addresses recommendation diversification. Existing diversification methods have a difficulty in dealing with the accuracy-diversity tradeoff. We propose a novel method to simultaneously optimize the user preference and diversity of k -items to be recommended.

Exploiting Diversification in Distributed Recommendation

Improving Recommendation Diversity Using Ranking-Based Techniques

Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the divers...

HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation

Location-based social networks such as Foursquare and Plancast have gained increasing popularity. On those sites, users can organize and participate in group activities; hence, recommending venues to a group is of practical importance. In this paper, we study the problem of recommending venues to groups of users and propose a Hierarchical Bayesian Model (HBGG) for this purpose. First, a generat...