# In short Short article about the use of cross-domain recommendation (ie use recommendation profile on a type of products to recommend products of another type) and in particular the gain when considering cold-start problems. The article is short and esay to read, experimental results do not allow to identify a clear trend, in particular concerning diversity related results. # Summary ### Introduction ##### usual solution to cold start pbs: * ask users directly * exploit additional information for example combining collaborative filtering with content-based rec ##### literature on the second possibility (cross-domain RS) * ref 15 (Winoto, Tang): conjecture that CD-RS may degrade accuracy but improve diversity (they test this assumption in the paper) * ref 11 (Sahebi, Brusilovski): quality of rec improve when domains are semantically connected ##### article assumption profile size (quantity of info) and diversity in source domain have an impact on accuracy in target domain with CD-rec ##### 3 research questions * improvement in terms of accuracy of CD rec for cold-start users? * is CD rec really useful to improve diversity? * what is the impact of size and diversity of the user profile in the source domain for the rec in the target domain? ### Experimental setting ##### Datasets: * Facebook likes on music and movies, metadata from DBPedia * likes only so dataset with positive feedback only * typical data entry: id, name, category, timestamp * disambiguation with DBPedia (technically challenging) ##### Rec algorithms evaluated * popularity based pop * CF user-based using nearest neighbor with Jaccard (unn) * CF item-based using nearest neighbor with Jaccard (inn) * CF item-based using matrix factorization (imf) * hybrid HeteRec (see ref 16) * hybrid PathRank (see ref 8) ##### Evaluation methodology * user-based 5-fold cross-validation strategy (see ref 7) * elaborate preprocessing (only users with > 16 likes..) * after preprocessing: music is 50K users, 5800 artists, 2M likes ; movies is 27K users, 4000 movies, 875.000 likes * quality estimators: Mean Reciprocal Rank for accuracy ; Intra-list diversity and Binomial diversity (see ref 14, Vargas et al.) for diversity ; also catalogue coverage ### Results ##### most results in Tab1 ##### Cross-domain recommendation accuracy (RQ1) * specific focus on cold-start situations (profile in target domain unknown or close to unknown) * case1: Music source, Movies target * CD-unn most accurate for users with cold start but perf strongly decreases as soon as target profile grows, would be caused by the choice of Jaccard as similarity metric (which is unreliable in cold start situations) * in terms of accuracy, only inn and imf benefit from Music feedback * in terms of coverage, only unn benefit from Music feedback * case2: Movies source, Music target * CD-unn again less performing when increasing profile size * coverage: same trend as case1 * summary: CD rec may be useful in cold start situation; some methods are much more efficient when using only source domain rather than source domain + a few info from target domain (result which should be explored more) ##### Cross-domain recommendation diversity (RQ2) * binomial diversity and ILD follow similar trends * case1: in general, CD rec brings less diversity * case2: opposite trend, most CD rec brings more diversity ##### Size and diversity of source domain user profiles (RQ3) * groups users by 20 likes intervals in the source domain, and ILD quartiles * compute average MRR as a function of these categories * results on Fig1, focusing on cold start targets (few or no likes in the target domain) * observation: improvement with profile size in source domain (left panel) [not surprising] * observation: best results obtained for very focused in terms of diversity * interpretation: RS chosen have a hard time finding inter-domain correlations, in particular from Music to Movies * conclusion: user profile in source and target domains are important for rec * remark: CD-inn has better perf than other RS in many scenarios considered