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2020-10-12
Puspitaningrum, Diyah, Fernando, Julio, Afriando, Edo, Utama, Ferzha Putra, Rahmadini, Rina, Pinata, Y..  2019.  Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk. 2019 7th International Conference on Cyber and IT Service Management (CITSM). 7:1–6.
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=5,7,9 (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 p@1, p@3, and p@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.