Cold-Start Management with Cross-Domain Collaborative Filtering and Tags

Abstract

Abstract. Recommender systems suffer from the new user problem, i.e., the difficulty to make accurate predictions for users that have rated only few items. Moreover, they usually compute recommendations for items just in one domain, such as movies, music, or books. In this paper we deal with such a cold-start situation exploiting cross-domain recommendation techniques, i.e., we suggest items to a user in one target domain by using ratings of other users in a, completely disjoint, auxiliary domain. We present three rating prediction models that make use of information about how users tag items in an auxiliary domain, and how these tags correlate with the ratings to improve the rating prediction task in a different target domain. We show that the proposed techniques can effectively deal with the considered cold-start situation, given that the tags used in the two domains overlap.

Alleviating the new user problem in collaborative filtering by exploiting personality information

“…This problem has been addressed from various perspectives in different research areas. It has been tackled by means of user preference aggregation and media-tion strategies for the cross-system personalization problem in user modeling [1,3,67,12], as a potential solution to mitigate the cold-start and sparsity problems in recommender systems [16,68,71,24] and as a practical application of knowledge transfer in machine learning [27,45,55].…”

“…However, regardless of the specific variant that is used, CF methods have a common limitation: the so called new user cold-start problem, which occurs when a system cannot generate personalized and relevant recommendations for a user who has just registered into the system. Although many solutions have been proposed [23,24,33,56,58,72,47,69], this problem is still challenging, and there is not a unique solution for it that can be applied to any domain or situation. Indeed, as we shall show later, different approaches better suit specific situations, e.g., when the new user has entered either zero or only a few ratings.…”

“…The goal of these systems is to transfer useful knowledge from auxiliary domains to the target domain in order to build in the target domain better rating predictions and item recommendations. Recently, cross-domain recommendation techniques have been applied to address the new user problem, by leveraging the knowledge of common rating patterns [27,45,55], shared social tags [68,24], and linked semantic concepts [37] in the source and target domains. To the best of our knowledge, our cross-domain recommendation method represents the first attempt to exploit user personality as a "bridge" to transfer user preference knowledge between domains, and address cold-start situations in the target domain.…”

Switching hybrid for cold-starting context-aware recommender systems

“…However, acquiring such information usually requires some additional effort of the users and/or service providers. This effort can be mitigated by employing an Active Learning (AL) strategy [8] or a cross-domain recommendation technique [9]. Another alternative, which we considered in our work, is to exploit a set of metadata describing the users and items (e.g., demographics and item descriptions), and to utilise them in a hybrid CARS in order to overcome the new user and new item problem, respectively [14].…”

Regularized deep learning for fashion and social media data analysis

“…The common information include but not limited to semantic networks, item attributes, inter-domain correlations and item attributes [33,34]; the second is to share the use or item latent features which could link source and target domains knowledge [35,36,37,38,39,40]; the third is to explicitly or implicitly the common rating patterns in source and target domain [41,42,43,44,45]. …”