Scalable and interpretable product recommendations via overlapping co-clustering

We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201

Farmer Cooperatives: Commercial Farmer Members and Use

Seventy-eight percent of commercial farmers were either members or nonmember patrons of marketing/farm supply cooperatives in 1986 compared with more than 76 percent in 1980. From 1980 to 1986, the percentage of commercial farmers who were members of cooperatives increased from 65 to 66 percent. Nonmember patrons held steady at 12 percent. The biggest change was an increase in percent of members among commercial farmers with sales of $500,000 and over. Members among this group increased from 56 percent in 1980 to 69 percent in 1986. The percentage of commercial farmers with multiple memberships increased and the percentage of farmers with inactive memberships decreased. The percentage using cooperatives for marketing and for purchasing increased. Forty-nine percent used a cooperative for marketing, and 71 percent used a cooperative to purchase farm supplies in 1986. Data for the study were obtained from surveys by the National Agricultural Statistics Service (formerly Statistical Reporting Service), U.S. Department of Agriculture.Farmer cooperatives, marketing cooperatives, farm supply cooperatives, cooperative members, commercial farmer, Agribusiness,