Cross-domain Collaborative Filtering Research Articles

Extracting latently overlapping users by graph neural network for non-overlapping cross-domain recommendation

Cross-domain collaborative filtering (CDCF) is an effective solution to alleviate the data sparsity problem. Most of existing CDCF methods rely on overlapping data, such as users, items or both. But in some realistic scenes, detection and accessibility of overlapping data are difficult or even impossible, which poses a pressing demand for researches on cross-domain recommendation without overlapping data. There actually have been some attempts on addressing this problem by sharing cluster-level rating pattern across source and target domains. But these solutions require explicit ratings, which makes them not suitable for more common implicit feedback recommendation. To address this problem, we propose a novel CDCF model for non-overlapping data scenarios, which adaptively extracts latently overlapping users of source and target domains from all users to build an implicit bridge for knowledge transfer. Specifically, we first design a self-supervised classifier guided by inter-domain contrastive learning to divide domain users into distinct groups based on their preference differences. Then, we perform graph convolution operations on the subgraph formed by such group users and their interactive items to explicitly mine the higher-order collaborative relationships between users and items. Finally, we construct sparse and reasonable implicit bridges between domains by designing flow-aware similarity measures for selective knowledge transfer among the extracted latently overlapping users. Extensive experiments on four public datasets demonstrate the superior performance of our proposed model over several state-of-the-art graph-based single- and cross-domain models.

Cross-Domain Collaborative Filtering: A Deep Neural Network Approach for Accurate and Diverse Recommendations

The rapid proliferation of data and the intricate nature of user behavior in the online realm have presented new hurdles for recommendation systems, which aim to suggest pertinent items to users. Among the notable challenges, ensuring the provision of accurate recommendations across diverse domains stands out. This research paper proposes an innovative solution to tackle this challenge by developing a cross-domain recommendation system that leverages the collaborative filtering technique to generate precise and varied recommendations spanning multiple domains. The system gathers user behavior and item attribute data from various domains and employs a collaborative filtering algorithm integrated with a deep neural network. The model makes use of a Neural Network with CReLU activation along with the embeddings of the user and the item followed by concealed layers. The output layer employs tanh activation to guarantee that recommendations fall within [-1, 1]. Adam optimizer, MSE loss, and an accuracy metric are utilised in training. This architecture captures user-item interactions effectively, resulting in precise personalised recommendations resulting an accuracy of 96.5% - 97.5%. To validate the effectiveness of the model, extensive evaluations are conducted on real-world datasets encompassing movies and books. The results demonstrate that the proposed system outperforms state-of-the-art recommendation systems in terms of accuracy, as measured by precision(89%), recall(90%), and AUC(0.74) scores. Furthermore, the system exhibits robust performance even in scenarios characterized by cold-start problems and data sparsity.

A selective ensemble learning based two-sided cross-domain collaborative filtering algorithm

Recently, various Cross-Domain Collaborative Filtering (CDCF) algorithms are presented to address the sparsity problem, leveraging ratings of auxiliary domains to improve target domain’s recommendation performance. Therein, two-sided CDCF algorithms have shown better performance, given the fact that they can extract both user and item information. However, as the auxiliary domains are not all related to the target domain, utilizing information from all the auxiliary domains may not be optimal and would lead to low efficiency. A Two-Sided CDCF model based on Selective Ensemble learning considering both Accuracy and Efficiency (TSSEAE) is proposed to balance recommendation accuracy and efficiency. In TSSEAE, user-sided and item-sided auxiliary domains are firstly combined to improve performance of target domain. Then, CDCF problems are converted to ensemble learning problems, with each combination corresponding to a classifier. In this way, the problem of selecting combinations can be converted to that of selecting classifiers, which is a selective ensemble learning problem. Finally, a bi-objective optimization problem is solved to obtain Pareto optimal solutions for the selective ensemble learning problem. The experimental result on Amazon dataset shows the effectiveness of TSSEAE.

A mixed heterogeneous factorization model for non-overlapping cross-domain recommendation

Cross-domain collaborative filtering has attracted a great deal of attention for its capability of dealing with data sparsity by transferring valuable knowledge from auxiliary domains to assist in recommendation in a target domain. State-of-the-art research has mainly focused on the scenario in which auxiliary domains share users or items with a target domain. However, such auxiliary data is rare or the acquisition of them is limited due to privacy concerns in real-world applications. We investigate a more realistic scenario in which auxiliary domains have neither users nor items overlapped with a target domain, thereby facilitating the collection of auxiliary data. In order to extract and transfer the sharing knowledge between auxiliary domains and target domain, we assume the user-item feedback in each domain consist of two parts: domain-transferrable information containing the shared knowledge and domain-reserved information reflecting the domain-specific characteristics. Correspondingly, we propose a mixed heterogeneous factorization model to capture the sharing knowledge and the domain-specific characteristics based on adapted tensor factorization and biased matrix factorization respectively and then combine them together in an accumulative way. Meanwhile, three types of domain heterogeneity including preference heterogeneity, characteristics heterogeneity and rating bias are taken into account in this model. Experimenting on four publicly available datasets across different domains, we show that our model is superior to state-of-the-art methods in rating prediction and top-N recommendation tasks.

Auxiliary Domain Selection in Cross-Domain Collaborative Filtering

The problem of data sparsity largely limits the accuracy of recommender systems in collaborative filtering model. To alleviate the problem, cross-domain collaborative filteri ng was proposed by harnessing the information from the auxiliary domains. Previous works mainly focused on improving the model of utilizing the auxiliary information yet little on the selection of auxiliary domains, although it is observed that the result of recommendation depends on the characteristics of auxiliary dataset . In this paper, we study the validity of cross-domain collaborative filtering by movie recommendation via different auxiliary domains of different movie genres. Through extensive experiments we find that the numbe r of overlapping users between target domain and auxiliary domain is an indicator of choosing beneficial domains, while the low Ku llback-Leibler divergence between non-overlapping user ratings, rather than the overlapping user ratings, is much more significant. The results are helpful in selection of auxiliary domains in cross-domain collaborative filtering.

CDLFM: cross-domain recommendation for cold-start users via latent feature mapping

Collaborative filtering (CF) is a widely adopted technique in recommender systems. Traditional CF models mainly focus on predicting the user preference to items in a single domain, such as the movie domain or the music domain. A major challenge for such models is the data sparsity, and especially, CF cannot make accurate predictions for the cold-start users who have no ratings at all. Although cross-domain collaborative filtering (CDCF) is proposed for effectively transferring knowledge across different domains, it is still difficult for existing CDCF models to tackle the cold-start users in the target domain due to the extreme data sparsity. In this paper, we propose the cross-domain latent feature mapping (CDLFM) model for the cold-start users in the target domain. Firstly, in order to alleviate the data sparsity in single domain and provide essential knowledge for next step, we take users’ rating behaviors into consideration and propose the matrix factorization by incorporating user similarities. Next, to transfer knowledge across domains, we propose the neighborhood-based cross-domain latent feature mapping method. For each cold-start user, we learn his/her feature mapping function based on his/her neighbor linked users. By adopting gradient boosting trees and multilayer perceptron to model the cross-domain feature mapping function, two CDLFM models named CDLFM-GBT and CDLFM-MLP are proposed. Experimental results on two real datasets demonstrate the superiority of our proposed model against other state-of-the-art methods.

LSCD: Low-rank and sparse cross-domain recommendation

Due to the ability of addressing the data sparsity and cold-start problems, Cross-Domain Collaborative Filtering (CDCF) has received a significant amount of attention. Despite significant success, most of the existing CDCF algorithms assume that all the domains are correlated, which is however not always guaranteed in practice. In this paper, we propose a novel CDCF algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms, LSCD extracts a user and an item latent feature matrix for each domain respectively, rather than tri-factorizing the rating matrix of each domain into three low dimensional matrices. In order to simultaneously improve the performance of recommendations among correlated domains by transferring knowledge and among uncorrelated domains by differentiating features in different domains, the features of users are separated into shared and domain-specific parts adaptively. Specifically, a low-rank matrix is used to capture the shared features of each user across different domains and a sparse matrix is used to characterize the discriminative features in each specific domain. Extensive experiments on two real-world datasets have been conducted to confirm that the proposed algorithm transfers knowledge in a better way to improve the quality of recommendation and outperforms state-of-the-art recommendation algorithms.

A User-Based Cross Domain Collaborative Filtering Algorithm Based on a Linear Decomposition Model

Sparsity is a tough problem in a single domain collaborative filtering (CF) recommendation system as it is difficult to compute the similarities among users accurately. Recently, cross domain CF is a new way to alleviate this difficulty. In this paper, we propose a user-based cross domain CF algorithm based on a linear decomposition model. We pour the items together and learn a linear decomposition model to explore the relationship between the total similarity and the local similarities of different domains. We first construct training samples by computing the similarities of any two users in different domains. Then, we solve a linear least square problem to obtain the decomposition coefficients. Finally, we compute the local similarity in the target domain using the decomposition model. Since we compute the similarity in the target domain with the help of rich ratings in other domains, this similarity would be expected to be more accurate than the measured similarity computed by the sparse ratings in the target domain. We conduct extensive experiments to show that the proposed algorithm is effective in addressing the data sparsity problem, as compared with many state-of-the-art CF methods.

Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains

In the real-world environment, users have sufficient experience in their focused domains but lack experience in other domains. Recommender systems are very helpful for recommending potentially desirable items to users in unfamiliar domains, and cross-domain collaborative filtering is therefore an important emerging research topic. However, it is inevitable that the cold-start issue will be encountered in unfamiliar domains due to the lack of feedback data. The Bayesian approach shows that priors play an important role when there are insufficient data, which implies that recommendation performance can be significantly improved in cold-start domains if informative priors can be provided. Based on this idea, we propose a Weighted Irregular Tensor Factorization (WITF) model to leverage multi-domain feedback data across all users to learn the cross-domain priors w.r.t. both users and items. The features learned from WITF serve as the informative priors on the latent factors of users and items in terms of weighted matrix factorization models. Moreover, WITF is a unified framework for dealing with both explicit feedback and implicit feedback. To prove the effectiveness of our approach, we studied three typical real-world cases in which a collection of empirical evaluations were conducted on real-world datasets to compare the performance of our model and other state-of-the-art approaches. The results show the superiority of our model over comparison models.

Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

Cross-domain based Event Recommendation using Tensor Factorization

Abstract Context in the form of meta-data has been accredited as an important component in cross-domain collaborative filtering (CDCF). In this research paper CDCF concept is used to exploit event information (context) from two UI matrices to allow the recommendation performance of one domain (Facebook- User-Event Matrix) to benefit from the information from another domain (Bookmyshow- Event-Tag Matrix). The model based collaborative filtering approach Tensor Factorization(TF) has been used to integrate Facebook provided User-Event context information with Bookmyshow Event-Tag context information to recommend events. In contrast to the standard collaborative tag recommendation, our CDCF approach uses one User-Event matrix of Facebook that takes another Bookmyshow Event-Tag matrix as additional informant. The proposed cross-domain based Event Recommendation approach is divided into three modules- i) data collection which extracts the unstructured dataset from the two domains Bookmyshow and social networking site Facebook using API’s; ii) data mapping module which is basically used to integrate the common knowledge/ data that can be shared between considered different domains (Facebook & Bookmyshow). This module integrates and reduces the data into structured events’ instances. As the dataset was collected from two different sites, an intersection of both was taken out. Therefore this module is carefully designed according to reliability of information that is common between two domains; iii) 3 order tensor factorization and Latent Dirichlet Allocation (LDA) used for most preferable recommendation by less pertinent result reduction. The proposed 3 order tensor factorization is designed for maximizing the mutual benefit from both the considered domains (organizer and user). Therefore providing three recommendations: For organizers: 1) system recommends places to conduct specific event according to maximum of attendees of a particular type of event at a specific location; 2) recommending target audience to organizer: those who are interested to attend event on the basis of past data for promotion purposes. For users: 3) recommending events to users of their interest on the basis of past record. Our result shows significant improvement in reduction of less relevant data and result effectiveness is measured through recall and precision. Reduction of less relevant recommendation is 64%, 72% and 63% for place recommendation to organizer, target audience recommendation to organizer and event recommendation to user respectively. The proposed tensor factorization approach achieved 68% precision, 15.5% recall in recommending attendees to organizer and 62% precision, 13.4% recall for event recommendation to user.

Rating knowledge sharing in cross-domain collaborative filtering.

Cross-domain collaborative filtering (CF) aims to share common rating knowledge across multiple related CF domains to boost the CF performance. In this paper, we view CF domains as a 2-D site-time coordinate system, on which multiple related domains, such as similar recommender sites or successive time-slices, can share group-level rating patterns. We propose a unified framework for cross-domain CF over the site-time coordinate system by sharing group-level rating patterns and imposing user/item dependence across domains. A generative model, say ratings over site-time (ROST), which can generate and predict ratings for multiple related CF domains, is developed as the basic model for the framework. We further introduce cross-domain user/item dependence into ROST and extend it to two real-world cross-domain CF scenarios: 1) ROST (sites) for alleviating rating sparsity in the target domain, where multiple similar sites are viewed as related CF domains and some items in the target domain depend on their correspondences in the related ones; and 2) ROST (time) for modeling user-interest drift over time, where a series of time-slices are viewed as related CF domains and a user at current time-slice depends on herself in the previous time-slice. All these ROST models are instances of the proposed unified framework. The experimental results show that ROST (sites) can effectively alleviate the sparsity problem to improve rating prediction performance and ROST (time) can clearly track and visualize user-interest drift over time.