Bayesian Personalized Ranking Model Research Articles

Neural_BPR: Multi-processing popularity bias mitigating method in recommendation systems

E-commerce recommendations address the problem of information overload, but recent research has identified the phenomenon of popularity bias in recommendation mechanisms. This phenomenon tends to homogenise recommendation results, i.e. popular products are widely recommended and non-popular products are under-exposed. The persistent presence of popularity bias in recommendation systems can lead to several negative consequences: (1) Consumers are unable to attain satisfactory personalized shopping experiences. (2) Small and medium enterprises are deprived of fair competitive opportunities, making survival challenging. (3) The user base of e-commerce platforms dwindles, leading to reduced transaction volumes. In this paper, we propose a multi-process fusion debiasing method. Convolutional neural networks are used to extract feature information to be embedded in the recommendation algorithm for pre-processing, in-processing and post-processing in turn to mitigate popularity bias and simultaneously enhance recommendation utility. The effectiveness of the proposed debiasing method is validated using the Bayesian Personalized Ranking (BPR) recommendation model as an example. Experimental results on two public datasets demonstrate that our model outperforms two baseline models and a state-of-the-art model. Specifically, compared to the traditional BPR model, the alleviation of popularity bias is around 70% to 80%, and the recommendation utility increases by approximately 30% to 50%.

Personalized recommendation with hybrid feedback by refining implicit data

In personalized recommender systems, the collaborative filtering (CF) recommendation approaches have been widely used to predict the preferences of users in real-world applications. Among them, Bayesian personalized ranking (BPR) attracts much attention as it can easily explore the binary form of implicit feedback. However, it still suffers from the absence problem of negative feedback. To address this issue, this paper proposes a hybrid-feedback collaborative filtering model by jointly exploiting the explicit and implicit feedback. Based on the assumption that users prefer items with high ratings, this work firstly introduces the definition of explicit rating data to the BPR model and further proposes an improved Bayesian personalized ranking (IBPR) model to jointly extract the implicit feedback features of users and items. The IBPR model alleviates the problem of lack of negative feedback and promotes the anti-noise performance of the recommender system. Then the IBPR and BiasSVD (Biased Singular Value Decomposition) models are combined to further extract explicit latent features of users as well as items and construct the hybrid-feedback CF model. In this model, the user–item ranking matrix is reconstructed based on the extracted implicit feedback features, and the rating matrix is constructed based on the extracted explicit feedback features. Our proposed method is evaluated on five public datasets and achieves the competitive performance.

Multi-modal visual adversarial Bayesian personalized ranking model for recommendation

• We propose two multi-modal datasets for recommendation. • We propose a novel recommendation system called the MVABPR . • We design a set of EMK features to boost recommendation performance. • We use cross-modal semantics and adversarial learning to promote performance. • The implicit feeling tone of a recommended item can be captured accurately. Recommendation system is facing the “data sparseness” issue. Additional information, including images, texts, and videos, contributes to alleviating this issue. We propose a new multi-modal visual adversarial Bayesian personalized ranking (MVABPR) model to address the issue. The proposed model takes new features, cross-modal semantics, adversarial learning, and visual interface into account. Two multi-modal datasets are created based on the MovieLens datasets and the correlated images. Besides the shape, texture, color, and deep learning-based features, a set of efficient match kernel features are proposed. More discriminative but low-dimensional cross-modal semantics among these features is mined to characterize each item effectively, which is absorbed into the MVABPR model through a visual interface. A new adversarial learning strategy is employed to optimize the whole training procedure. This makes the MVABPR model more robust and stable. Experimental results demonstrate that the MVABPR model is effective and robust for recommendation. It outperforms other competitive baselines. As another advantage, it can learn visual information and users’ rating jointly, effectively, combined with adversarial learning. And the implicit feeling tone of a recommended item can be accurately captured by the proposed model. More importantly, the model achieves better performance on a large-scale sparser dataset, demonstrating its higher practicality.

Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user–item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but- not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.

SDBPR: Social distance-aware Bayesian personalized ranking for recommendation

Recommendation systems recommend new items to users. Because training data contain only binary forms of implicit feedback in many cases, such as in IoT and IoV, one-class collaborative filtering, which can be solved by using rating-based methods to estimate the numeric scores of items or ranking-based methods based on the preferences of each user for items, must be addressed. In addition, because of the sparsity of such data, ranking-based methods are often preferred over rating-based methods when only implicit feedback is available. Social information has recently been used to improve the accuracy of rankings. Traditional approaches simply consider the direct friends of users in a social network, but this process fails to consider the propagation of influence along connections in the social network and cannot reveal the complex graph structure of the social network. In this paper, a novel social distance-aware Bayesian personalized ranking model, called SDBPR, is proposed to generate more accurate recommendations. SDBPR uses a random walk to travel the social network and then makes pairwise assumptions about the ranking order based on the distance between users along the random walk. The experimental results on two real datasets show that the proposed approaches significantly outperform the baseline approaches in terms of ranking prediction.

Exploiting geo-social correlations to improve pairwise ranking for point-of-interest recommendation

Recently, as location-based social network (LBSN) rapidly grow, point-of-in-terest (POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-in-terests (POIs) on the actual output of recom-mender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model (GSB-PR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.

Social Trust Aware Item Recommendation for Implicit Feedback

Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Specifically, we first introduce the social trust assumption — a user’s taste is close to the neighbors he/she trusts — into the Bayesian Personalized Ranking model. To explore the impact of users’ multi-faceted trust relations, we further propose a categorysensitive random walk method CRWR to infer the true trust value on each trust link. Finally, we arrive at our trust strength aware item recommendation method SocialBPRCRWR by replacing the raw binary trust matrix with the derived real-valued trust strength. Data analysis and experimental results on two real-world datasets demonstrate the existence of social trust influence and the effectiveness of our social based ranking method SocialBPRCRWR in terms of AUC (area under the receiver operating characteristic curve).