  Statistics based privacy-aware recommender systems make suggestions more
powerful by extracting knowledge from the log of social contacts interactions,
but unfortunately, they are static. Moreover, advice from local experts
effective in finding specific business categories in a particular area. We
propose a dynamic recommender algorithm based on a lazy random walk that
recommends top-rank shopping places to potentially interested visitors. We
consider local authority and topical authority. The algorithm tested on
FourSquare shopping data sets of 5 cities in Indonesia with k-steps of 5,7,9 of
(lazy) random walks and compared the results with other state-of-the-art
ranking techniques. The results show that it can reach high score precisions
(0.5, 0.37, and 0.26 respectively on precision at 1, precision at 3, and
precision at 5 for k=5). The algorithm also shows scalability concerning
execution time. The advantage of dynamicity is the database used to power the
recommender system; no need to be very frequently updated to produce a good
recommendation.
