Name
Abstract | ||
|---|---|---|
Cross-domain recommender systems adopt different techniques to transfer learning from source domain to target domain in order to alleviate the sparsity problem and improve accuracy of recommendations. Traditional techniques require the two domains to be linked by shared characteristics associated to either users or items. In collaborative filtering (CF) this happens when the two domains have overlapping users or item (at least partially). Recently, Li et al. [7] introduced codebook transfer (CBT), a cross-domain CF technique based on co-clustering, and presented experimental results showing that CBT is able to transfer knowledge between non-overlapping domains. In this paper, we disprove these results and show that CBT does not transfer knowledge when source and target domains do not overlap. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2645710.2645769 | RecSys |
Keywords | Field | DocType |
information filtering,accuracy measures,matrix factorization,cold start,sparsity,ratings aggregation | Recommender system,Information system,Collaborative filtering,Computer science,Matrix decomposition,Transfer of learning,Artificial intelligence,Machine learning,Codebook | Conference |
Citations | PageRank | References |
7 | 0.45 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Cremonesi | 1 | 1306 | 87.23 |
| Massimo Quadrana | 2 | 239 | 13.89 |