Towards Explaining Latent Factors with Topic Models in Collaborative Recommender Systems

Rossetti, Marco; Stella, Fabio; Zanker, Markus

doi:10.1109/dexa.2013.26

Cited by 36 publications

(20 citation statements)

References 20 publications

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“…State-of-the-art approches to generate recommendations from positive ratings only are often based on standard matrix factorization. However, they offer low interpretability because "latent factors obtained with mathematical methods applied to the user-item matrix can be hardly interpreted by humans" [35]. Similar observations have been made in several works [3], [36], [51].…”

Section: Introductionmentioning

confidence: 56%

“…Item-and user-based techniques yield a reasoning of the sort "similar users have also bought", but are often outperformed by latent factor models, such as matrix factorization approaches [20]. Matrix factorization techniques in their traditional form predict ratings or preferences well, but the latent features make it difficult to explain a recommendation [3], [36], [35]. Recently, [51] investigated a method for explainable factorization by extracting explicit factors (sentiment, keywords, etc) from user reviews; however such an approach is applicable only in the presence of additional textual information for each training example.…”

Section: Related Workmentioning

confidence: 99%

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Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering

Heckel

Vlachos

Parnell

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).

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Section: Introductionmentioning

confidence: 56%

Section: Related Workmentioning

confidence: 99%

Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering

Heckel

Vlachos

Parnell

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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show abstract

“…Finally, we recommend the items whose ratings are higher than others for the user [11]. The common models of CF include Bayesian Networks [12], latent factor models [13], Singular Value Decomposition (SVD) [14], matrix factorization [15], and Probabilistic Latent Semantic Analysis (PLSA) [16]. In these methods, the matrix factorization method is the most widely used, because it can effectively extract some latent features between users and items with the matrix decomposition [17].…”

Section: Introductionmentioning

confidence: 99%

“…Yuan Zhang et al proposed a method based on graph and the label applying to recommendation systems [20]. The successful experiment results of these methods show the CF based on graph that has some advantages compared to the existing methods [1], because they can learn more latent information in the network [13]. However, most of the methods based on the graph are nonlinear, which predict the users' ratings of unknown items by using a single classifier.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Collaborative Filtering with MLP

Chen

Zhou

et al. 2018

Mathematical Problems in Engineering

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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 predict the ratings for items, so we can achieve better effects for recommendation.

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“…In the recommendation area, a prominent approaches is the collaborative filtering (CF) [22] that recommends items based on ratings of users with common interests to the target user. The state-of-theart in recommendation field is formed by latent factor models [18], where some of the most successful implementations are based on Matrix Factorization (MF) [12]. In its basic form, the MF characterizes users and items with vectors of latent factors inferred from the pattern of the rated items.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Method to Reduce the Running Time and Accelerate the k-Nearest Neighbors Search

Costa

Sousa

Ramos

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Recommendation systems are software tools and techniques that provide customized content to users. The collaborative filtering is one of the most prominent approaches in the recommendation area. Among the collaborative algorithms, one of the most popular is the k-Nearest Neighbors (kNN) which is an instance-based learning method. The kNN generates recommendations based on the ratings of the most similar users (nearest neighbors) to the target one. Despite being quite effective, the algorithm performance drops while running on large datasets. We propose a method, called Restricted Space kNN that is based on the restriction of the neighbors search space through a fast and efficient heuristic. The heuristic builds the new search space from the most active users. As a result, we found that using only 15% of the original search space the proposed method generated recommendations almost as accurate as the standard kNN, but with almost 58% less running time.

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Towards Explaining Latent Factors with Topic Models in Collaborative Recommender Systems

Cited by 36 publications

References 20 publications

Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering

Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering

Graph-Based Collaborative Filtering with MLP

A Collaborative Method to Reduce the Running Time and Accelerate the k-Nearest Neighbors Search

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