Multi-Domain Collaborative Filtering

By Yu Zhang, Bin Cao, Dit-Yan Yeung. 2010

179 citations recorded by the source corpus.

Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.

Subfields: Information Systems, Management Science and Operations Research

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Citations in the corpus

An Overview of Multi-task Learning (2018, neutral): “Web applications based on MTL include learning to rank in web searches [121], web search ranking [122], multi-domain collaborative filtering [123], behavioral targeting [124], and conversion maximization in display advertising [125].”
A Survey on Multi-Task Learning (2017, methodology): “In [185], a multi-domain collaborative filtering method based on matrix”
Multitask Matrix Completion for Learning Protein Interactions Across Diseases (2016, unknown): “There have also been multitask approaches on link prediction (Singh and Gordon, 2008; Li et al., 2009; Cao et al., 2010; Zhang et al., 2012); however, many of those models do not incorporate features of the nodes in the graph.”
Transfer learning in heterogeneous collaborative filtering domains (2013, background): “Most existing transfer learning methods in collaborative filtering consider auxiliary data from several perspectives, including user-side transfer [39,11,58,38,55], item-side transfer [52], or knowledge-transfer using related but not aligned data [31, 32].”
Cross-Domain Recommender Systems (2015, background): “• Domain correlating, which aims to factorize single-domain rating matrices using latent features, explore correlations between latent features in single domains, and transfer knowledge between domains through such correlations [13, 59, 67].”
A General Cross-Domain Recommendation Framework via Bayesian Neural Network (2018, background): “by leveraging the rating information from other domains to enhance the prediction on the target domain [7], [17].”
Feature-based transfer learning with real-world applications (2010, background): “Web applications of transfer learning include text classification across domains [151, 47, 48, 72, 195, 207, 36, 204, 213, 203], advertising [40, 39], learn to rank across domains [10, 192, 35, 68] and multi-domain collaborative filtering [103, 102, 29, 223, 143].”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “Similarly, multidomain CF is an approach that extends PMF to multiple domains involving explicit user preference [Zhang et al. 2010] or implicit user feedback [Tang et al.”
Artificial intelligence in recommender systems (2020, background): “A new probabilistic multi-objective evolutionary algorithm was proposed in [118] that strikes a good balance between accuracy and diversity, in which a new crossover operator called multi-parent probability genetic operator and a new topic diversity indicator were introduced.”
Circle-based recommendation in online social networks (2012, background): “predict users’ interests by mining user rating history data [1], [2], [4], [6], [16], [23] and [24].”
Web Data Extraction, Applications and Techniques: A Survey (2012, methodology): “2009b] and, finally, other approaches project the user and item space in the various domains in a shared latent space by means of regularization techniques [Zhang et al. 2010].”
Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, methodology): “To address these challenges, in [ Zhang et al. , 2012 ] , Zhang et al. proposed a multi-domain collaborative ﬁltering (MCF) framework for solving the data sparsity problem in multiple domains.”
Cross-Domain Recommendation: An Embedding and Mapping Approach (2017, background): “Cross-domain recommendation was proposed to combat the long-standing data sparsity problem, which leverages feedbacks or ratings from multiple domains to improve recommendation accuracy in a collective manner [Singh and Gordon, 2008; Zhang et al., 2012].”
Cross-domain Recommendation via Deep Domain Adaptation (2018, background): “On the other hand, collaborative ltering (CF) approaches were also proposed [16, 40].”
Collaborative Filtering-Based Recommendation of Online Social Voting (2017, background): “Many OSNs now offer the social voting function, through which a user can share with friends her opinions, e.g., like or dislike, on various subjects, ranging from user statuses, profile pictures, to games played, Manuscript received October 22, 2014; revised August 19, 2016; accepted January 24,…”
Personalized Recommendation Combining User Interest and Social Circle (2014, unknown): “It can be viewed as the first generation of RSes [7] with traditional collaborative filtering algorithms [8], [9], [10], [11], [12], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32] to predict user interest.”
Nonparametric bayesian multitask collaborative filtering (2013, methodology): “In our experiments, apart from our method, we also evaluate two existing multitask CF methods related to our work, namely the MCF-LF method of [34], and the CMF method of [28].”
Multi-relational matrix factorization using bayesian personalized ranking for social network data (2012, background): “Zhang et al [17] contribute an extension of the probabilistic matrix factorization to the multi-relational case.”