A Graph-Based Method for Combining Collaborative and Content-Based Filtering (original) (raw)
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A Survey on Graph-Based Collaborative Filtering Techniques in Recommender Systems
International Journal of Knowledge Based Computer Systems, 2019
Recommender Systems (RS) are software tools which can be used in making useful predictions of items to users. RS has been an important research area since the mid-1990s, and there are a lot of RS tools built since then to improve user satisfaction. While building the RS tools, researchers face a lot of problems in the form of data sparsity, information overload, cold-start, scalability, lack of resources and time. These factors may reduce the accuracy of predictions. To overcome these problems, researchers model the rating data as graphs. Through graphs, we can explore the transitive associations and hidden information in our dataset. Particularly, this work focuses only on the graph-based collaborative filtering (GBCF) techniques introduced in the recommendation systems. We have studied and analyzed various GBCF articles published in the most popular online digital libraries during the last two decades. These approaches have been categorized into three broader categories: user-item, user-user, and item-item based on the model of the graph, which is used for the recommendation purpose. This survey provides an understanding of how graph-based CF has helped researchers and developers built a more efficient RS model in terms of different RS goals, such as accuracy.
Unifying collaborative and content-based filtering
Proceedings of the Twenty First International Conference on Machine Learning, 2004
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.
Hydra: a hybrid recommender system [cross-linked rating and content information]
Proceeding of the 1st ACM international …, 2009
This paper discusses the combination of collaborative and contentbased filtering in the context of web-based recommender systems. In particular, we link the well-known MovieLens rating data with supplementary IMDB content information. The resulting network of user-item relations and associated content features is converted into a unified mathematical model, which is applicable to our underlying neighbor-based prediction algorithm. By means of various experiments, we demonstrate the influence of supplementary user as well as item features on the prediction accuracy of Hydra, our proposed hybrid recommender. In order to decrease system runtime and to reveal latent user and item relations, we factorize our hybrid model via singular value decomposition (SVD).
Simplifying Graph-based Collaborative Filtering for Recommendation
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
Graph Convolutional Networks (GCNs) are a popular type of machine learning models that use multiple layers of convolutional aggregation operations and non-linear activations to represent data. Recent studies apply GCNs to Collaborative Filtering (CF)-based recommender systems (RSs) by modeling user-item interactions as a bipartite graph and achieve superior performance. However, these models face difficulty in training with non-linear activations on large graphs. Besides, most GCN-based models could not model deeper layers due to the over-smoothing effect with the graph convolution operation. In this paper, we improve the GCN-based CF models from two aspects. First, we remove non-linearities to enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we obtain the initialization of the embedding for each node in the graph by computing the network embedding on the condensed graph, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse interaction data. The proposed model is a linear model that is easy to train, scalable to large datasets, and shown to yield better efficiency and effectiveness on four real datasets. CCS CONCEPTS • Theory of computation → Graph algorithms analysis.
Graph-Based Collaborative Filtering with MLP
Mathematical Problems in Engineering, 2018
The collaborative filtering (CF) methods are widely used in the recommendation systems. They learn users’ interests and preferences from their historical data and then recommend the items users may like. However, the existing methods usually measure the correlation between users by calculating the coefficient of correlation, which cannot capture any latent features between users. In this paper, we proposed an algorithm based on graph. First, we transform the users’ information into vectors and use SVD method to reduce dimensions and then learn the preferences and interests of all users based on the improved kernel function and map them to the network; finally, we predict the user’s rating for the items through the Multilayer Perceptron (MLP). Compared with existing methods, on one hand, our method can discover some latent features between users by mapping users’ information to the network. On the other hand, we improve the vectors with the ratings information to the MLP method and p...
n Implementation of the User-based Collaborative Filtering Algorithm
The explosive growth and availability of data on the internet has caused information overload. Searching for a query is not easy in the sources of information available for the interest of an individual user. Collaborative filtering systems recommend items based upon opinions of people with similar tastes. Collaborative filtering overcomes some difficulties faced by traditional information filtering by eliminating the need for computers to understand the content of the items. Further, collaborative filtering can also recommend articles that are not similar in content to items rated in the past as long as like-minded users have rated the items. Collaborative filtering (CF) is one of the most frequently used techniques in personalized recommendation systems. But currently used CF techniques are based on item rating prediction. We proposed an improved personalized recommended CF algorithm. Hybrid recommender systems or content-boosted technologies are quickly produce high quality recommendations. We have explored content-boosted CF technique which analyzes the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different Memorybased CF and Model-based CF techniques. Finally, we experimentally evaluate our results and compare them. The testing results show that in most cases, the improved algorithm that we put forward can improve recommendation quality.
Learning to Recommend with User Generated Content
In the era of Web 2.0, user generated content (UGC), such as social tag and user review, widely exists on the Internet. However, in recommender systems, most of existing related works only study single kind of UGC in each paper, and different types of UGC are utilized in different ways. This paper proposes a unified way to use different types of UGC to improve the prediction accuracy for recommendation. We build two novel collaborative filtering models based on Matrix Factorization (MF), which are oriented to user features learning and item features learning respectively. In the user side, we construct a novel regularization term which employs UGC to better understand a user’s interest. In the item side, we also construct a novel regularization term to better infer an item’s characteristic. We conduct comprehensive experiments on three real-world datasets, which verify that our models significantly improve the prediction accuracy of missing ratings in recommender systems.
2014
Content-based and collaborative filtering methods are the most successful solutions in recommender systems. Content based method is based on items attributes. This method checks the features of users favourite items and then proposes the items which have the most similar characteristics with those items. Collaborative filtering method is based on the determination of similar items or similar users, which are called item-based and user-based collaborative filtering, respectively.In this paper we propose a hybrid method that integrates collaborative filtering and content-based methods. The proposed method can be viewed as user-based Collaborative filtering technique. However to find users with similar taste with active user, we used content features of the item under investigation to put more emphasis on users rating for similar items. In other words two users are similar if their ratings are similar on items that have similar context. This is achieved by assigning a weight to each ra...
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests
Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization - UMAP '19, 2019
Collaborative Filtering is largely applied to personalize item recommendation but its performance is a ected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks. In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Speci cally, we aim at testing whether the integration of frequently co-occurring interests in information search logs can improve recommendation performance in User-to-User Collaborative Filtering (U2UCF). For this purpose, we propose the Extended Category-based Collaborative Filtering (ECCF) recommender, which enriches category-based user pro les derived from the analysis of rating behavior with data categories that are frequently searched together by people in search sessions. We test our model using a big rating dataset and a log of a largely used search engine to extract the co-occurrence of interests. e experiments show that ECCF outperforms U2UCF and categorybased collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix Factorization algorithm in accuracy and diversity of recommendation lists.