6533b838fe1ef96bd12a521f
RESEARCH PRODUCT
Cross-Domain Recommendations with Overlapping Items
Denis KotkovShuaiqiang WangJari Veijalainensubject
ta113Information retrievaldata collectionComputer sciencesuosittelujärjestelmät02 engineering and technologyDomain (software engineering)020204 information systemscollaborative filtering0202 electrical engineering electronic engineering information engineeringcross-domain recommendationscontent-based filtering020201 artificial intelligence & image processingrecommender systemsdescription
In recent years, there has been an increasing interest in cross-domain recommender systems. However, most existing works focus on the situation when only users or users and items overlap in different domains. In this paper, we investigate whether the source domain can boost the recommendation performance in the target domain when only items overlap. Due to the lack of publicly available datasets, we collect a dataset from two domains related to music, involving both the users’ rating scores and the description of the items. We then conduct experiments using collaborative filtering and content-based filtering approaches for validation purpose. According to our experimental results, the source domain can improve the recommendation performance in the target domain when only items overlap. However, the improvement decreases with the growth of non-overlapping items in different domains. peerReviewed
year | journal | country | edition | language |
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2016-01-01 |