Sparsity Problem In Collaborative Filtering Research Articles

A Recommendation Algorithm Combining Local and Global Interest Features

Due to the ability of knowledge graph to effectively solve the sparsity problem of collaborative filtering, knowledge graph (KG) has been widely studied and applied as auxiliary information in the field of recommendation systems. However, existing KG-based recommendation methods mainly focus on learning its representation from the neighborhood of target items, ignoring the influence of other items on the target item. The learning focuses on the local feature representation of the target item, which is not sufficient to effectively explore the user’s preference degree for the target item. To address the above issues, in this paper, an approach combining users’ local interest features with global interest features (KGG) is proposed to efficiently explore the user’s preference level for the target item, which learns the user’s local interest features and global interest features for target item through Knowledge Graph Convolutional Network and Generative Adversarial Network (GAN). Specifically, this paper first utilizes the Knowledge Graph Convolutional Network to mine related attributes on the knowledge graph to effectively capture item correlations and obtain the local feature representation of the target item, then uses the matrix factorization method to learn the user’s local interest features for target items. Secondly, it uses GAN to learn the user’s global interest features for target items from the implicit interaction matrix. Finally, a linear fusion layer is designed to effectively fuse the user’s local and global interests towards target items to obtain the final click prediction. Experimental results on three real datasets show that the proposed method not only effectively integrates the user’s local and global interests but also further alleviates the problem of data sparsity. Compared with the current baselines for knowledge graph-based systems, the KGG method achieves a maximum improvement of 8.1% and 7.6% in AUC and ACC, respectively.

SimDCL: dropout-based simple graph contrastive learning for recommendation

Representation learning of users and items is the core of recommendation, and benefited from the development of graph neural network (GNN), graph collaborative filtering (GCF) for capturing higher order connectivity has been successful in the recommendation domain. Nevertheless, the matrix sparsity problem in collaborative filtering and the tendency of higher order embeddings to smooth in GNN limit further performance improvements. Contrastive learning (CL) was introduced into GCF and alleviated these problems to some extent. However, existing methods usually require graph perturbation to construct augmented views or design complex CL tasks, which limits the further development of CL-based methods in the recommendation. We propose a simple CL framework that does not require graph augmentation, but is based on dropout techniques to generate contrastive views to address the aforementioned problem. Specifically, we first added dropout operation to the GNN computation, and then fed the same batch of samples twice into the network for computation. Using the randomness of dropout, a pair of views with random noise was obtained, and maximizing the similarity of the view pairs is set as an auxiliary task to complement the recommendation. In addition, we made a simple modification to the computation of the GNN to alleviate the information loss due to embedding smoothing by means of cross-layer connected graph convolution computation. We named our proposed method as Simple Contrastive Learning Graph Neural Network based on dropout (SimDCL). Extensive experiments on five public datasets demonstrate the effectiveness of the proposed SimDCL, especially on the Amazon Books and Ta-Feng datasets, where our approach achieves 44% and 43% performance gains compared to baseline.

A Point-of-Interest Recommendation Method Exploiting Sequential, Category and Geographical Influence

Point of interest (POI) recommendation as an important service in location-based social networks has developed rapidly, which can help users find more interesting unknown locations and facilitate service providers to provide users with more accurate notifications or advertisements. Some existing work has addressed the data sparsity problem of collaborative filtering by incorporating contextual information into the model. However, they ignore the sequence relationship contained in the user’s historical check-in records, which makes it difficult to accurately model the user’s preference and affects the final recommendation results. To acquire users’ preference for a location more accurately, this paper proposes a new POI recommendation framework exploiting sequential, category, and geographical influence. Firstly, we obtain the latent vector of POI and the latent vector of the user’s preference for POI from the user’s check-in sequence based on the word embedding model. Next, a virtual common access sequence for users is constructed according to the user’s check-ins, a new similarity computation method is present combining category differentiation and POI latent vector. Then, we apply it to the collaborative filtering framework to get the user’s behavioral preference probability of POI. In addition, the kernel density estimation method is employed to get the user’s geographical preference probability of POI by considering the geographical influence. Finally, the POI recommendation list is obtained by the weighted fusion of the two users’ preference probability to improve the performance of the POI recommendation. Experimental results on two datasets indicate that the proposed method has better performance in terms of three evaluation metrics than the other five POI recommendation methods.

Transfer Learning in Collaborative Filtering with Uncertain Ratings

To solve the sparsity problem in collaborative filtering, researchers have introduced transfer learning as a viable approach to make use of auxiliary data. Most previous transfer learning works in collaborative filtering have focused on exploiting point-wise ratings such as numerical ratings, stars, or binary ratings of likes/dislikes. However, in many real-world recommender systems, many users may be unwilling or unlikely to rate items with precision.In contrast, practitioners can turn to various non-preference data to estimate a range or rating distribution of a user's preference on an item.Such a range or rating distribution is called an uncertain rating since it represents a rating spectrum of uncertainty instead of an accurate point-wise score. In this paper, we propose an efficient transfer learning solution for collaborative filtering, known as {\em transfer by integrative factorization} (TIF), to leverage such auxiliary uncertain ratings to improve the performance of recommendation. In particular, we integrate auxiliary data of uncertain ratings as additional constraints in the target matrix factorization problem, and learn an expected rating value for each uncertain rating automatically. The advantages of our proposed approach include the efficiency and the improved effectiveness of collaborative filtering, showing that incorporating the auxiliary data of uncertain ratings can really bring a benefit. Experimental results on two movie recommendation tasks show that our TIF algorithm performs significantly better over a state-of-the-art non-transfer learning method.

Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering

Probabilistic matrix factorization (PMF) is the most popular method among low-rank matrix approximation approaches that address the sparsity problem in collaborative filtering for recommender systems. PMF depends on the classical maximum a posteriori estimator for estimating model parameters; however, these approaches are vulnerable to overfitting because of the nature of a single point estimation that is pursued by these models. An alternative approach to PMF is a Bayesian PMF model that suggests the Markov chain Monte Carlo algorithm as a full estimation for approximate intractable posterior over model parameters. However, despite its success in increasing prediction, it has a high computational cost. To this end, we proposed a novel Bayesian deep learning-based model treatment, namely, variational autoencoder Bayesian matrix factorization (VABMF). The proposed model uses stochastic gradient variational Bayes to estimate intractable posteriors and expectation–maximization-style estimators to learn model parameters. The model was evaluated on the basis of three MovieLens datasets, namely, Ml-100k, Ml-1M, and Ml-10M. Experimental results showed that our proposed VABMF model significantly outperforms state-of-the-art RS.

A Reliable Solution for Sparsity Problem in Collaborative Filtering Using Demographic Approach

A Reliable Solution for Sparsity Problem in Collaborative Filtering Using Demographic Approach

Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering

Memory-based collaborative filtering selects the top- k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced. The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity. In this paper, we analyze various problems with the traditional neighbor selection method and propose a novel method to improve upon them. The proposed method minimizes the similarity evaluation errors with the existing neighbor selection method by considering the number of common items between two objects. The method is effective for the practical application of collaborative filtering. For validation, we analyze and compare experimental results between an existing method and the proposed method. We were able to confirm that the proposed method can improve the prediction accuracy of memory-based collaborative filtering by neighbor selection that prioritizes the number of common items.

Integrated Preference Similarity Algorithm for Improving Sparsity Problem in Collaborative Filtering

협업필터링은 유사 사용자들 간의 집단지성에 기반한 사회적 추천 알고리즘으로 전자상거래 시스템에 도입되면서 상업적으로 활용되어 왔다. 반면, 평가행렬의 희박성, 확장성, 신규 추천 등의 한계가 있으며 이에 대한 개선 연구가 지속적으로 이루어지고 있다. 그 예로 복합 추천 알고리즘은 주로 사용자 프로필 정보 또는 아이템 속성 등 내용정보와 협업필터링을 결합하여 단점을 보완하고자 하였다. 기존 복합 알고리즘 중 사용자 프로필 정보, 아이템 속성정보, 아이템 평가행렬을 모두 통합하는 연구는 없었으며, 본 연구에서는 특히 사용자간의 공통평가 아이템 수가 적어 추천이 어려운 희박성 문제를 개선하기 위하여 통합 유사도 알고리즘을 제안하고 기존의 방법과의 성능을 비교한다.

Alleviating the sparsity problem of collaborative filtering using rough set

PurposeThe purpose of this paper is to introduce an improved nearest‐neighbor collaborative filtering algorithm based on rough set theory to alleviate the sparsity problem of collaborative filtering. With experimentations, the new algorithm is thereafter evaluated.Design/methodology/approachNearest‐neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, and its recommendation quality is seriously influenced by the sparsity of user ratings. By using rough set theory, the nearest‐neighbor collaborative filtering algorithm can be improved in the sparsity data situation. The union of user rating items is used as the basis of similarity computing among users, and then a rating predicting method based on rough set theory is proposed to estimate missing values in the union of user rating items for decreasing sparsity.FindingsThe sparsity problem of collaborative filtering can be alleviated by using the union of user rating items and estimating missing values based on rough set theory. The experimental results show that the new algorithm can efficiently improve recommendation quality of collaborative filtering.Originality/valueThe union of user rating items was used as the basis of similarity computing among users. A rating prediction method based on rough set theory with an assistant method was proposed to complete the missing values in the union of user rating items. Orthogonal list was used to storage user‐item ratings matrix.

Transfer Learning in Collaborative Filtering for Sparsity Reduction

Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem.

Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score. We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may "dilute" the data used to infer user preferences and lead to degradation in recommendation performance.