Enhancing Personalisation Using Dynamic Context-Aware and Content-Based Cross Domain Recommender System

Cross Domain Recommendation Systems using Deep Learning: A Systematic Literature Review

Recommendation systems (RS) play an important role in directing customers to their favorite items. Data sparsity, which usually leads to overfitting, is a major bottleneck for making precise recommendations. Several cross-domain RSs have been proposed in the past decade in order to reduce the sparsity issues via transferring knowledge. However, existing works only focus on either nearest neighbor model or latent factor model for cross domain scenario. In this paper, we introduce a Multifaceted Cross-Domain Recommendation System (MCDRS) which incorporates two different types of collaborative filtering for cross domain RSs. The first part is a latent factor model. In order to utilize as much knowledge as possible, we propose a unified factorization framework to combine both CF and content-based filtering for cross domain learning. On the other hand, to overcome the potential inconsistency problem between different domains, we equip the neighbor model with a selective learning mechanism so that domain-independent items gain more weight in the transfer process. We conduct extensive experiments on two real-world datasets. The results demonstrate that our MCDRS model consistently outperforms several state-of-the-art models.

Traditional recommender systems suffer from the data sparsity problem. However, user knowledge acquired in one domain can be transferred and exploited in several other relevant domains. In this context, cross-domain recommender systems have been proposed to create a new and effective recommendation paradigm in which to exploit rich data from auxiliary domains to assist recommendations in a target domain. Before knowledge transfer takes place, building reliable and concrete domain correlation is the key ensuring that only relevant knowledge will be transferred. Social tags are used to explicitly link different domains, especially when neither users nor items overlap. However, existing models only exploit a subset of tags that are shared by heterogeneous domains. In this paper, we propose a complete tag-induced cross-domain recommendation (CTagCDR) model, which infers interdomain and intradomain correlations from tagging history and applies the learned structural constraints to regularize joint matrix factorization. Compared to similar models, CTagCDR is able to fully explore knowledge encoded in both shared and domain-specific tags. We demonstrate the performance of our proposed model on three public datasets and compare it with five state-of-the-art single and cross-domain recommendation approaches. The results show that CTagCDR works well in both rating prediction and item recommendation tasks, and can effectively improve recommendation performance.

Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations.Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS.Besides, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard.Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems.The DaGzang platform works according to the complete loop, and it consists of the following three steps: i) detecting the overlap association (data distribution pattern) between the real-world datasets, ii) generating synthetic datasets based on these overlap associations, and iii) evaluating the quality of the generated synthetic datasets.The real-world datasets in our experiments were collected from Amazon's e-commercial website.To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi.We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF.Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS.In particular, the highest performance of the three recommendation methods is User-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE).

Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data. The limited presence of shared users often hampers the effective modeling of common preferences. While leveraging shared items’ attributes, such as category and popularity, can enhance cross-domain recommendation performance, the scarcity of shared items between domains has limited research in this area. To address this, we propose a Coherence-guided Preference Disentanglement (CoPD) method aimed at improving cross-domain recommendation by (i) explicitly extracting shared item attributes to guide the learning of shared user preferences and (ii) disentangling these preferences to identify specific user interests transferred between domains. CoPD introduces coherence constraints on item embeddings of shared and specific domains, aiding in extracting shared attributes. Moreover, it utilizes these attributes to guide the disentanglement of user preferences into separate embeddings for interest and conformity through a popularity-weighted loss. Experiments conducted on real-world datasets demonstrate the superior performance of our proposed CoPD over existing competitive baselines, highlighting its effectiveness in enhancing cross-domain recommendation performance. The code is available at https://github.com/XiangZongyi/CoPD .

This chapter presents a brief and systematic overview of four major advanced recommender systems: group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. These advanced recommendations are characterized and compared in a unifying model as extensions of basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed. Advanced recommender technologies will continue to advance.

Cross-domain recommendation is critically important to construct a practical recommender system. The challenges of building a cross-domain recommender system lie in both the data sparsity issue and lacking of sufficient semantic information. Traditional approaches focus on using the user-item rating matrix or other feedback information, but the contents associated with the objects like reviews and the relationships among the objects are largely ignored. Although some works merge the content information and the user-item rating network structure, they only focus on using the attributes of the items but ignore user generated contents such as reviews. In this paper, we propose a novel cross-domain recommender framework called ECHCDR (Embedding content and heterogeneous network for cross-domain recommendation), which contains two major steps of content embedding and heterogeneous network embedding. By considering the contents of objects and their relationships, ECHCDR can effectively alleviate the data sparsity issue. To enrich the semantic information, we construct a weighted heterogeneous network whose nodes are users and items of different domains. The weight of link is defined by an adjacency matrix and represents the similarity between users, books and movies. We also propose to use adversarial training method to learn the embeddings of users and cross-domain items in the constructed heterogeneous graph. Experimental results on two real-world datasets collected from Amazon show the effectiveness of our approach compared with state-of-art recommender algorithms.

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations.

CD-CARS: Cross-domain context-aware recommender systems

Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. When new users join the system, it will take some time before they enter some ratings in the system, until that time, there are not enough ratings to learn the matrix factorization model. Using auxiliary data such as user’s demographic, ratings and reviews in relevant domains, is an effective solution to reduce the new user problem. In this paper, we used the data of users activity from auxiliary domains to build domain-independent users representation that could be used to predict users ratings in the target domains. We proposed an iterative method which applied MAX-VAR generalized canonical correlation analysis (GCCA) on user’s latent factors learned from matrix factorization on each domain. Also, to improve the capability of GCCA to learn latent factors for new users, we propose a generalized canonical correlation analysis by inverse sum of selection matrices (GCCA-ISSM) approach, which provides better recommendations in cold-start scenarios. The proposed approach is extended using content-based features like topic models extracted from user’s reviews. We demonstrate the accuracy and effectiveness of the proposed approaches on cross-domain rating predictions using comprehensive experiments on Amazon and MovieLens datasets.

Cross domain recommendation and preferences association are emerging research topics. In this paper, we will study the two topics through experimental analysis methods: firstly, we use folksonamy to analyze the preferences association among different domains; secondly, we analyze the feasibility of cross domain rating prediction based on KNN model. The experimental results report the associative tag pairs of users' preferences on items across domains. In addition, we report the cross domain prediction results here.

In recent years, the recommender system has been widely used in online platforms, which can extract useful information from giant volumes of data and recommend suitable items to the user according to user preferences. However, the recommender system usually suffers from sparsity and cold-start problems. Cross-domain recommendation, as a particular example of transfer learning, has been used to solve the aforementioned problems. However, many existing cross-domain recommendation approaches are based on matrix factorization, which can only learn the shallow and linear characteristics of users and items. Therefore, in this article, we propose a novel autoencoder framework with an attention mechanism (AAM) for cross-domain recommendation, which can transfer and fuse information between different domains and make a more accurate rating prediction. The main idea of the proposed framework lies in utilizing autoencoder, multilayer perceptron, and self-attention to extract user and item features, learn the user and item-latent factors, and fuse the user-latent factors from different domains, respectively. In addition, to learn the affinity of the user-latent factors between different domains in a multiaspect level, we also strengthen the self-attention mechanism by using multihead self-attention and propose AAM++. Experiments conducted on two real-world datasets empirically demonstrate that our proposed methods outperform the state-of-the-art methods in cross-domain recommendation and AAM++ performs better than AAM on sparse and large-scale datasets.

Sliced Wasserstein based Canonical Correlation Analysis for Cross-Domain Recommendation

Data sparsity and data imbalance are practical and challenging issues in cross-domain recommender systems (RSs). This paper addresses those problems by leveraging the concepts which derive from representation learning, adversarial learning, and transfer learning (particularly, domain adaptation). Although various transfer learning methods have shown promising performance in this context, our proposed novel method RecSys-DAN focuses on alleviating the cross-domain and within-domain data sparsity and data imbalance and learns transferable latent representations for users, items, and their interactions. Different from the existing approaches, the proposed method transfers the latent representations from a source domain to a target domain in an adversarial way. The mapping functions in the target domain are learned by playing a min-max game with an adversarial loss, aiming to generate domain indistinguishable representations for a discriminator. Four neural architectural instances of ResSys-DAN are proposed and explored. Empirical results on real-world Amazon data show that, even without using labeled data (i.e., ratings) in the target domain, RecSys-DAN achieves competitive performance as compared to the state-of-the-art supervised methods. More importantly, RecSys-DAN is highly flexible to both unimodal and multimodal scenarios, and thus it is more robust to the cold-start recommendation which is difficult for the previous methods.

The tagging system provides users with a platform to express their preferences as they annotate terms or keywords to items. Tag information is a bridge between two domains for transferring knowledge and helping to alleviate the data sparsity problem, which is a crucial and challenging problem in most recommender systems. Existing methods incorporate correlations extracted from overlapping tags at a lexical level in cross-domain recommendation, but they neglect semantical relationships between different tags, which impairs prediction accuracy in the target domain. To solve this challenging problem, we propose a cross-domain recommendation method with semantic correlation in tagging systems. This method automatically captures the semantic relationships between non-identical tags and applies them to the recommendation. The word2vec technique is used to learn the latent representations of tags. Semantically equivalent tags are then grouped to form a joint embedding space comprised of tag clusters. This embedding space serves as the bridge between domains. By mapping users and items from both the source and target domains into the same embedding space, similar users or items across domains can be identified. Thus, the recommendation in a sparse target domain is improved by transferring knowledge through correlated users and items. Experimental results with three datasets on six cross-domain recommendation tasks demonstrate that the proposed method exploits the semantic links from tags in two domains and outperforms five benchmarks in prediction accuracy. The results indicate that transferring knowledge through tags semantics is feasible and effective.