Cross-Domain Collaborative Recommendation in a Cold-Start Context: The Impact of User Profile Size on the Quality of Recommendation

Sahebi, Shaghayegh; Brusilovsky, Peter

doi:10.1007/978-3-642-38844-6_25

Cited by 42 publications

(28 citation statements)

References 7 publications

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“…There are different approaches for addressing cross domain recommendation. One approach is to assume that different domains share similar set of users but not the items, as illustrated in [20]. In their work, the authors augmented data from rating of movies and books from datasets that have common users.…”

Section: Related Workmentioning

confidence: 99%

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

Elkahky

Song

2015

Proceedings of the 24th International Conference on World Wide Web

576

189

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Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for model- * Work done at Microsoft Research during the author's summer internship. ing cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.

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Section: Related Workmentioning

confidence: 99%

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

Elkahky

Song

2015

Proceedings of the 24th International Conference on World Wide Web

576

189

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“…Another study with positive results was conducted by Abel et al The dataset contained information related to the same users from 7 different OSNs [1]. Sahebi and Brusilovsky demonstrated the usefulness of recommendations based on source domains to overcome cold start problem [18].…”

Section: Cross-domain Recommendationsmentioning

confidence: 99%

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

Kotkov

Wang

Veijalainen

2017

Lecture Notes in Business Information Processing

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Abstract. Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. According to our results, the source domain can improve serendipity in the target domain for both approaches. The source domain decreases accuracy for contentbased filtering and increases accuracy for collaborative filtering. The improvement of accuracy decreases with the growth of non-overlapping items in different domains.

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“…Another study with positive results was conducted by Abel et al The dataset contained information related to the same users from 7 different OSNs (Abel et al, 2013). Sahebi et al demonstrated the usefulness of recommendations based on additional domains to overcome cold start problem (Sahebi and Brusilovsky, 2013). Most works on cross-domain recommender systems focus on the situation when users or both users and items of several domains overlap (Cantador and Cremonesi, 2014).…”

Section: Related Workmentioning

confidence: 99%

Cross-Domain Recommendations with Overlapping Items

Kotkov

Wang

Veijalainen

2016

Proceedings of the 12th International Conference on Web Information Systems and Technologies

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Abstract: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.

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Cross-Domain Collaborative Recommendation in a Cold-Start Context: The Impact of User Profile Size on the Quality of Recommendation

Cited by 42 publications

References 7 publications

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

Cross-Domain Recommendations with Overlapping Items

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