| Title | Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Puspitaningrum, Diyah, Fernando, Julio, Afriando, Edo, Utama, Ferzha Putra, Rahmadini, Rina, Pinata, Y. |
| Conference Name | 2019 7th International Conference on Cyber and IT Service Management (CITSM) |
| Keywords | data privacy, dynamic recommender algorithm, Expert Systems and Privacy, FourSquare shopping data sets, Human Behavior, human factors, lazy random walk, local expert, pagerank, pubcrawl, recommender systems, Scalability, social contacts interactions, Statistics, statistics based privacy-aware recommender systems, top-rank shopping places |
| Abstract | 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. |
| DOI | 10.1109/CITSM47753.2019.8965354 |
| Citation Key | puspitaningrum_finding_2019 |
Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk