Name
Abstract | ||
|---|---|---|
Recommendation systems are becoming increasingly important, as evidenced by the popularity of the Netflix prize and the sophistication of various online shopping systems. With this increase in interest, a new problem of nefarious or false rankings that compromise a recommendation system's integrity has surfaced. We consider such purposefully erroneous rankings to be a form of "toxic waste," corrupting the performance of the underlying algorithm. In this paper, we propose an adaptive reweighted algorithm as a possible approach towards correcting this problem. Our algorithm relies on finding a low-rank-plus-sparse decomposition of the recommendation matrix, where the adaptation of the weights aids in rejecting the malicious contributions. Simulations suggest that our algorithm converges fairly rapidly and produces accurate results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638932 | 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Adaptive optimization, sparsity, convergence, toxic waste | Recommender system,Computer science,Popularity,Toxic waste,Artificial intelligence,Compromise,Machine learning,Sparse matrix,Sophistication | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam S. Charles | 1 | 113 | 10.21 |
| Ahmed Ali | 2 | 26 | 11.76 |
| Aditya Joshi | 3 | 171 | 21.14 |
| Stephen Conover | 4 | 1 | 0.35 |
| Christopher K. Turnes | 5 | 25 | 1.86 |
| Mark A. Davenport | 6 | 1171 | 66.72 |