Given a user in a social network, which new friends should we recommend, the dual goal being to achieve user satisfaction and good network connectivity? Similarly, which new products are better to recommend to satisfy customers’ taste/needs as well as increase vendor profit? Typical recommender systems use merely past purchases, product ratings, demographic meta-data, and network ‘proximity’ to make recommendations. This traditional approach, however, does not take into account the profitability of products to vendors in a customer-product network, or the efficacy of new links in a social network. We argue that it is more appropriate to view the problem of generating recommendations as an optimization problem. In this paper, (a) we propose Value Pick, a framework which incorporates the ‘value’ of recommendations into the system while still providing accurate recommendations that retain user trust; (b) our method is parsimonious (requires only a single parameter \tau), flexible (\tau is used to flexibly adjust the level of balance between ‘user satisfaction’ and ‘gain’), and general (can be used with any ‘value’ metric); and finally (c) we examine the problem in the social networks setting, simulate comprehensive experiments to compare our method to several basic heuristics, and show that Value Pick yields higher ‘gain’ while keeping user satisfaction high.
[1]
Mark Rosenstein,et al.
Recommending and evaluating choices in a virtual community of use
,
1995,
CHI '95.
[2]
Christos Faloutsos,et al.
Graphs over time: densification laws, shrinking diameters and possible explanations
,
2005,
KDD '05.
[3]
Matthew Brand,et al.
A Random Walks Perspective on Maximizing Satisfaction and Profit
,
2005,
SDM.
[4]
Luca Becchetti,et al.
An optimization framework for query recommendation
,
2010,
WSDM '10.
[5]
Paolo Toth,et al.
Knapsack Problems: Algorithms and Computer Implementations
,
1990
.
[6]
Long-Sheng Chen,et al.
Developing recommender systems with the consideration of product profitability for sellers
,
2008,
Inf. Sci..
[7]
Yehuda Koren,et al.
Collaborative filtering with temporal dynamics
,
2009,
KDD.
[8]
Kartik Hosanagar,et al.
Recommender systems and their impact on sales diversity
,
2007,
EC '07.
[9]
Claire Mathieu,et al.
Maximizing profit using recommender systems
,
2009,
ArXiv.