LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-overlapping Cross-Domain Collaborative Filtering

Zang, Yizhou; Hu, Xiaohua

doi:10.1007/978-3-319-71246-8_39

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…These experimental results are counter intuitive, because we expect no improvement from CrossFire when the source and the target domains are identical -indeed, there should be no useful checkin patterns to be transfered from the source domain to the target one, as no new information has been obtained. Note that none of previous works [12,13,15,20] on non-overlapping CDCF reports the effectiveness of their proposed approaches when setting the source domain equal to the target domain. In response to research question RQ1, our experimental results demonstrate that the CBT-based strategy of CrossFire does not clearly contribute to the improvements of the recommendation accuracy, compared to the traditional single-domain MF-based models.…”

Section: Resultsmentioning

confidence: 96%

“…The venue recommendation task is thus to rank those 101 venues for each user, aiming to rank highest the recent, ground truth checkin/rating. Note that previous works [12,13,15,20] on nonoverlapping CDCF use Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to evaluate the quality of rating prediction. In contrast, we evaluate the quality of recommendation in terms of Hit Ratio (HR) 6 and Normalised Discounted Cumulative Gain (NDCG) on the ranked lists of venues -as applied in previous studies [10,23,24].…”

Section: Datasets and Measuresmentioning

confidence: 99%

“…Apart from these additional sources of information and the sequential properties of users' interactions, recently, Cross-Domain Collaborative Filtering (CDCF) models have been proposed to alleviate the sparsity problem in a target domain [11][12][13][14][15]. CDCF aims to improve the quality of recommendations in a target domain by leveraging the knowledge extracted from source domains.…”

Section: Introductionmentioning

confidence: 99%

“…For example, a user who has both Twitter and Foursquare accounts, overlapping CDCF aims to transfer user's preferences extracted from the user's tweets on Twitter (source domain) to improve the venue recommendation on Foursquare (target domain). Non-overlapping CDCF models like [11][12][13]18] aim to transfer useful information from the source domain to the target one without any explicit links, which is the most challenging problem in cross-domain recommendation. An example of non-overlapping CDCF is a newly opened book e-commerce website that would like to build a recommender system.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the non-overlapping CDCF models [12,13,18] are based on a Codebook Transfer technique (CBT) proposed by Li et al [11]. CBT aims to transfer rating patterns from the source domain (e.g., the popular movie rating website) to the target domain (e.g., the newly opened book website).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

On Cross-Domain Transfer in Venue Recommendation

Manotumruksa

Rafailidis

Macdonald

et al. 2019

Lecture Notes in Computer Science

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Venue recommendation strategies are built upon Collaborative Filtering techniques that rely on Matrix Factorisation (MF), to model users' preferences. Various cross-domain strategies have been proposed to enhance the effectiveness of MF-based models on a target domain, by transferring knowledge from a source domain. Such cross-domain recommendation strategies often require user overlap, that is common users on the different domains. However, in practice, common users across different domains may not be available. To tackle this problem, recently, several cross-domains strategies without users' overlaps have been introduced. In this paper, we investigate the performance of state-of-theart cross-domain recommendation that do not require overlap of users for the venue recommendation task on three large Location-based Social Networks (LBSN) datasets. Moreover, in the context of cross-domain recommendation we extend a state-of-the-art sequential-based deep learning model to boost the recommendation accuracy. Our experimental results demonstrate that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and, further we validate this result on the latest sequential deep learning-based venue recommendation approach. Finally, for reproduction purposes we make our implementations publicly available.

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Section: Resultsmentioning

confidence: 96%

Section: Datasets and Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On Cross-Domain Transfer in Venue Recommendation

Manotumruksa

Rafailidis

Macdonald

et al. 2019

Lecture Notes in Computer Science

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Design and Evaluation of Cross-Domain Recommender Systems

Dacrema

Cantador

Fernández-Tobías

et al. 2012

Recommender Systems Handbook

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The proliferation of e-commerce sites and online social media has allowed users to provide preference feedback and maintain profiles in multiple systems, reflecting a spectrum of their tastes and interests. Leveraging all the user preferences available in several systems or domains may be beneficial for generating more encompassing user models and better recommendations, e.g., through mitigating the cold-start and sparsity problems, or enabling cross-selling recommendations for items from multiple domains. Cross-domain recommender systems, thus, aim to enhance recommendations in a target domain by exploiting knowledge from source domains. In this chapter, we formalize the cross-domain recommendation problem, unify the perspectives from which it has been addressed, and analytically categorize and describe various recommendation techniques, from the simple legacy ones to the sophisticated ones based on deep-learning.Authors Suppressed Due to Excessive Length Zalando recommends clothing, and Spotify recommends songs and playlists. These domain-specific systems have been successfully deployed by numerous websites, and the single-domain recommendation functionality is not perceived as a limitation, but rather pitched as a focus on a certain market segment.Nonetheless, large e-commerce sites like Amazon and Alibaba often store user feedback for items across multiple domains, and social media users often express their tastes and interests for a variety of topics. It may, therefore, be beneficial to leverage all the available user data provided in various systems and domains, in order to generate more encompassing user models and better recommendations. Instead of treating each domain (e.g., movies, books and music) independently, knowledge acquired in a source domain could be transferred to and exploited in another target domain. The research challenge of transferring knowledge and the business potential of delivering recommendations spanning multiple domains, have triggered an increasing interest in cross-domain recommendations.Consider two motivating use cases for cross-domain recommendations. The first refers to the well known cold-start problem, which makes it difficult to generate recommendations due to the lack of sufficient information about users or items. In a cross-domain setting, a recommender may draw on information acquired from other domains to alleviate such a problem, e.g., user's favorite movie genres may be derived from her favorite book genres. The second refers to the generation of personalized cross-selling or bundle recommendations for items from multiple domains, e.g., a movie accompanied by a music album similar to the movie soundtrack. This recommendation may be informed by the user's movie tastes, extracted from rating correlations within a joined movie-music rating matrix.These use cases are underpinned by an intuitive assumption that there are correspondences between user and item profiles in the source and target domains. This assumption has been validated in several marketing, behavioral, a...

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Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems

et al. 2022

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Despite many recent advances, state-of-the-art recommender systems still struggle to achieve good performance with sparse datasets. To address the sparsity issue, transfer learning techniques have been investigated for recommender systems, but they tend to impose strict constraints on the content and structure of the data in the source and target domains. For transfer learning methods to work well, there should normally be homogeneity between source and target domains, or a high degree of overlap between the source and target items. In this paper we propose a novel transfer learning framework for mitigating the effects of sparsity and insufficient data. Our method requires neither homogeneity nor overlap between the source and target domains. We describe and evaluate a shared parameter auto-encoder to jointly learn representations of user/item aspects in two domains, applying Maximum Mean Discrepancy (MMD) loss during training to ensure that the source and target representations are similar in the distribution space. The approach is evaluated using a number of benchmark datasets to demonstrate improved recommendation performance when learned representations are used in collaborative filtering. The code used for this work is available on github.com a . a https://github.com/doubleblind193819238/AutoShare INDEX TERMS recommender system, neural networks, transfer learning, domain adaptation

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LKT-FM: A Novel Rating Pattern Transfer Model for Improving Non-overlapping Cross-Domain Collaborative Filtering

Cited by 4 publications

References 18 publications

On Cross-Domain Transfer in Venue Recommendation

On Cross-Domain Transfer in Venue Recommendation

Design and Evaluation of Cross-Domain Recommender Systems

Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems

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